Zooplankton biodiversity monitoring in polluted freshwater ecosystems: A technical review

Journal Pre-proof Zooplankton Biodiversity Monitoring in Polluted Freshwater Ecosystems: A technical review Wei Xiong, Xuena Huang, Yiyong Chen, Ruiying Fu, Xun Du, Xingyu Chen, Aibin Zhan PII:

S2666-4984(19)30008-0

DOI:

https://doi.org/10.1016/j.ese.2019.100008

Reference:

ESE 100008

To appear in:

Environmental Science and Ecotechnology

Please cite this article as: W. Xiong, X. Huang, Y. Chen, R. Fu, X. Du, X. Chen, A. Zhan, Zooplankton Biodiversity Monitoring in Polluted Freshwater Ecosystems: A technical review, Environmental Science and Ecotechnology, https://doi.org/10.1016/j.ese.2019.100008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 The Author(s). Published by Elsevier B.V. on behalf of Chinese Society for Environmental SciencesHarbin Institute of TechnologyChinese Research Academy of Environmental Sciences.

1

Zooplankton Biodiversity Monitoring in Polluted Freshwater

2

Ecosystems: A technical review

3 4

Wei Xiong1, Xuena Huang1, Yiyong Chen1, Ruiying Fu1, Xun Du1, Xingyu Chen1,2, Aibin

5

Zhan1,3*

6 7

1

8

Shuangqing Road, Haidian District, Beijing 100085, China;

9

2

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18

College of Resources, Environment and Tourism, Capital Normal University, 105 West

10

Third Ring Road, Haidian District, Beijing 100048, China;

11

3

12

Road, Shijingshan District, Beijing 100049, China.

University of Chinese Academy of Sciences, Chinese Academy of Sciences, 19A Yuquan

13 14

*Corresponding authors:

15

Prof. Aibin Zhan; Email: [email protected] or [email protected]; Phone & Fax:

16

(+86)-10-6284-9882;

1

17

ABSRACT

18

Freshwater ecosystems harbor a vast diversity of micro-eukaryotes (rotifers, crustaceans and

19

protists), and such diverse taxonomic groups play important roles in ecosystem functioning

20

and services. Unfortunately, freshwater ecosystems and biodiversity therein are threatened by

21

many environmental stressors, particularly those derived from intensive human activities

22

such as chemical pollution. In the past several decades, significant efforts have been devoted

23

to halting biodiversity loss to recover services and functioning of freshwater ecosystems.

24

Biodiversity monitoring is the first and a crucial step towards diagnosing pollution impacts

25

on ecosystems and making conservation plans. Yet, bio-monitoring of ubiquitous

26

micro-eukaryotes is extremely challenging, owing to many technical issues associated with

27

micro-zooplankton such as microscopic size, fuzzy morphological features, and extremely

28

high biodiversity. Here, we review current methods used for monitoring zooplankton

29

biodiversity to advance management of impaired freshwater ecosystems. We discuss the

30

development of traditional morphology-based identification methods such as scanning

31

electron microscope (SEM) and ZOOSCAN and FlowCAM automatic systems, and

32

DNA-based strategies such as metabarcoding and real-time quantitative PCR. In addition, we

33

summarize advantages and disadvantages of these methods when applied for monitoring

34

impacted ecosystems, and we propose practical DNA-based monitoring workflows for

35

studying biological consequences of environmental pollution in freshwater ecosystems.

36

Finally, we propose possible solutions for existing technical issues to improve accuracy and

37

efficiency of DNA-based biodiversity monitoring.

38

Keywords: biodiversity, micro-eukaryotes, metabarcoding, freshwater ecosystem, water 2

39

pollution.

40

1 Introduction

41

1.1 Biodiversity loss in freshwater ecosystems

42

Since the first proposition of the term ‘biodiversity’ in the United Nations’ Conference

43

on Environment and Development in 1992, the importance of biodiversity has been well

44

recognized and conservation of biodiversity has drawn substantial attention (Warren 1996).

45

Among various types of ecosystems, freshwater ecosystems provide unique habitats,

46

supporting a high level of biodiversity. Freshwater ecosystems occupy only approximately

47

0.8% of the Earth’s surface but support almost 6% of all known species (Dudgeon et al. 2006).

48

For example, more than 10,000 fish species live in freshwater ecosystems, accounting for 40%

49

of the known global fish species (Cazzolla 2016). In addition, freshwater ecosystems provide

50

irreplaceable goods and services for human beings, such as drinking and irrigation water,

51

food, creation, and regulation of micro-climate (Vörösmarty et al. 2010; Green et al. 2015).

52

However, many factors, particularly those derived from anthropogenic activities such as

53

water pollution and invasive species, have largely degraded freshwater ecosystems over the

54

past several decades (Dudgeon et al. 2006; Bush et al. 2017). Freshwater ecosystems such as

55

rivers and inland lakes are among the most threatened ecosystems on the Earth (Vörösmarty

56

et al. 2010; Alahuhta et al. 2019). As a result, biodiversity loss in freshwater ecosystems is

57

much faster than that in terrestrial counterparts (Geist 2011). Even worse, biodiversity loss in

58

threaten freshwater ecosystems have not slowed down in recent years (Butchart et al. 2010),

59

despite the fact that great effort has been placed on maintaining or recovering biodiversity in

60

freshwater ecosystems. These efforts have been largely unsuccessful due to frequent 3

61

disturbance derived from increasing anthropogenic activities and knowledge gaps on

62

biodiversity in freshwater ecosystems (Clausen & York 2008).

63

Indeed, biodiversity loss in freshwater ecosystem is likely much more severe than we

64

have realized, as biological response to disturbance in freshwater ecosystems is not

65

completely known, especially on the widespread but hidden microscopic taxa such as

66

zooplankton (Cazzolla 2016). A large body of scientific literature has illustrated the

67

biodiversity loss of macro-eukaryotes under human activity disturbance, such as fishes,

68

amphibians, mollusks and crustaceans (Williams 2002; Stuart et al. 2004; Nobles & Zhang,

69

2011; Freyhof & Brooks 2017). Nevertheless, studies on biodiversity loss dynamics of

70

micro-eukaryotes are rare (Xiong et al. 2019). In terms of monitoring and conservation

71

priority, better-known macro-eukaryotes have drawn more attention than micro-eukaryotes

72

such as microscopic zooplankton (Darwall et al. 2009). Indeed, the highly neglected

73

microscopic zooplankton play vital ecological roles in aquatic food-webs, such as linking

74

phytoplankton and bacteria to high trophic levels such as fish (Johnson et al. 2011). The

75

protection and recovery of overlooked microscopic zooplankton biodiversity largely

76

determine the conservation of biodiversity at high trophic levels, as well as the integration

77

and functioning of freshwater ecosystems.

78

1.2 Potential indicative roles of zooplankton in freshwater ecosystems

79

Zooplankton include diverse taxa such as protists, rotifers, copepods and cladocerans,

80

many of which are microscopic (De Vargas et al. 2015). Multiple studies have made a

81

consistent and crucial realization that zooplankton taxa are rapid responders to many

82

environmental stressors, such as hydrological changes, climate changes and anthropogenic 4

83

activity-induced water pollution (Duggan et al. 2001; Pawlowski et al. 2016). Specifically,

84

previous laboratory or field studies have indicated that zooplankton communities were

85

significantly impacted by excessive loading of nutrients (Duggan et al. 2001; Wang et al.

86

2010; Xiong et al. 2019), and also negatively affected by microplastics (Scherer et al. 2018),

87

pesticides (Hanazato 2001), and pharmaceuticals and personal care products (PPCPs) (Garric

88

2013). As such, researchers have identified their usefulness as ecological indicators to water

89

pollution. For instances, rotifers are used to diagnose ecological impacts of freshwater

90

toxicants, such as endocrine disruptors, bioconcentration of lead, and nanoparticles toxicity

91

(Snell & Marcial 2017). Yang et al. (2017b) indicated that zooplankton communities could be

92

used to predict ecological thresholds of ammonia nitrogen. Payne (2013) listed and

93

recommended seven key reasons for the use of protists as good bio-indicators in aquatic

94

ecosystems. Azevêdo et al. (2015) showed that zooplankton communities played a

95

complementary role to macroinvertebrates in indicating variation of the trophic status of

96

waters. Thus, bio-monitoring zooplankton communities has become a widely accepted and

97

irreplaceable aspect in ecological conservation and management of aquatic ecosystems.

98

1.3 Technical difficulties for biodiversity monitoring of zooplankton

99

Despite rapid development of novel technologies in the past several decades, most

100

traditional morphology-based methods have been used to investigate interactions between

101

biodiversity of zooplankton and environmental pollution in freshwater ecosystems (Wang et

102

al. 2010; Xiong et al. 2016b). In recent years, several novel strategies, such as metabarcoding

103

(see section 3.1 in this article), represent novel and robust approaches to profile microscopic

104

organisms more efficiently and with finer resolution (Bucklin et al. 2016). Studies have 5

105

facilitated the use of metabarcoding for assessing diversity of many zooplankton

106

communities (Zhan et al. 2013, 2014b; Brown et al. 2015; Xiong et al. 2018), such as protists

107

(Pawlowski et al. 2016) and copepods (Hirai et al. 2015). Metabarcoding-based methods have

108

generated detailed information about biodiversity of zooplankton, providing important

109

support to biodiversity conservation, such as the development of DNA-based ecological

110

status assessment under the European Water Framework Directive (Hering et al. 2018), and

111

advancement of routine freshwater bio-monitoring by using molecular methods (Blackman et

112

al. 2019).

113

Yet, there remain many methodological difficulties for biodiversity monitoring of

114

zooplankton in both morphology-based and DNA-based strategies in polluted freshwater

115

ecosystems, and many taxonomic groups of zooplankton are largely undescribed or unknown

116

(Dudgeon et al. 2006). The primary issue for morphology-based biodiversity monitoring is

117

resolution and efficiency. When gathering information on zooplankton composition and

118

abundance in a traditional way, it relies heavily on taxonomists who identify specimens under

119

a binocular microscope. The process is time- and cost-consuming and requires high expertise.

120

Moreover, both the number of samples that can be analyzed and the availability of competent

121

taxonomists are limited. Meanwhile, relatively low resolution imposes limitations on species

122

identification, particularly at larval/juvenile stages and for cryptic species or species complex.

123

In addition, morphology-based methods are challenged by rare species detection (Zhan et al.

124

2013), which is of great significance for biological conservation. Rare species, which are

125

often either endangered species or species under severe environmental stresses in polluted

126

ecosystems, should be protected or analyzed in priority. 6

127

Such difficulties in accurate morphological identification of species not only impede

128

biodiversity monitoring in a traditional way, but also hinder biodiversity investigation by

129

using DNA-based approaches (Zhan et al. 2013; Xiong et al. 2016a). Despite that DNA-based

130

methods obviously advance the efficiency and resolution, particularly for studies aiming at

131

large geographical scales, the precondition for DNA-based methods relies on available

132

reference databases that must be constructed based on morphological identification (Briski et

133

al. 2011). In addition, there are challenges in quantification of taxon abundance in using

134

DNA-based methods such as metabarcoding, as metabarcoding analysis has not shown good

135

agreement between specie abundance data and sequence abundance (e.g., Sun et al. 2015).

136

1.4 Our aims

137

To advance management of impaired freshwater ecosystems, this review aims to provide

138

an overview of available methods for monitoring biodiversity of eukaryotic zooplankton,

139

including both morphology-based and DNA-based approaches. In addition, we discuss

140

technical difficulties, as well as causes and consequences of these difficulties in biodiversity

141

monitoring. Finally, we propose a practical monitoring workflow to study biological

142

consequences of environmental pollution in aquatic ecosystems as take-home information.

143

2 Morphology-based methods for zooplankton biodiversity monitoring

144

2.1 Traditional morphological methods

145

The acquisition of biodiversity data has historically been based on morphological

146

characterization of species. Researchers use many tools such as nets, pumps or water bottles

147

to collect specimens and gatherer formation of composition and abundance of species, and

148

then collected specimens are subjected for identification by taxonomists under a microscope 7

149

(Gorsky et al. 2010; EÁlvarez et al. 2013; Andújar et al. 2018b). This traditional technique is

150

considered to be useful for the identification and enumeration of microplankton (Fig. 1), and

151

thus providing invaluable information on species identification (Gorsky et al. 2010; EÁlvarez

152

et al. 2013). For instance, Humes (1994) have reported approximate 11500 morphological

153

species of copepods. Kreutz & Foissner (2006) edited a book of protozoological monographs,

154

recording 670 species of protists, micro-metazoans and bacteria in ponds of Simmelried in

155

Germany. Wang (1961) studied and recorded 252 freshwater rotifer species in China, which

156

laid the foundation for rotifer study in China.

157

With the advent of morphological identification by electron microscopes, the scanning

158

electron microscope (SEM) has become a powerful analytical tool (Fig. 2). The use of SEM

159

significantly increases the resolution in specimen identification and helps detect taxonomy

160

keys. For instance, by using SEM, several rotifer species with differentiated trophi were

161

identified in an apparently morphological uniform genus (Segers 1995). Papa et al. (2012)

162

updated taxonomic status of crustacean zooplankton in a lake in Philippines using a

163

combination of light and scanning electron microscopes. Hines (2019) studied the

164

biogeography of freshwater ciliates (protozoa) in Florida, USA with a high resolution of

165

species identification by SEM. Thus, SEM has become an important complementary tool for

166

morphology-based monitoring of zooplankton, particularly on microscopic taxa with debates.

167

2.2 New morphological identification methods

168

It is obvious that both traditional microscopes and SEMs can detect biological features

169

in detail but fail to analyze a large number of species rapidly. With the advent of digital

170

imaging and scanning technologies, several automatic or semiautomatic instruments have 8

171

been developed to rapidly analyze zooplankton samples. The two representative ones are

172

ZOOSCAN system (Gorsky et al. 2010) and Flow Ctometer And Microscope (FlowCAM)

173

(EÁlvarez et al. 2013). ZOOSCAN takes digital images of zooplankton samples and analyze

174

images based on available databases in a fast and (semi-)automatic way. By integrating

175

ZooProcess and Plankton Identifier software, ZOOSCAN system can acquire and classify

176

digital zooplankton images from collected zooplankton communities by comparing scanned

177

images of each organism with a reference library. Meanwhile, the biomass and accurate body

178

size of each species can be estimated by ZOOSCAN system for a quantitative study of

179

zooplankton samples. However, ZOOSCAN is suitable for organisms with body size ranging

180

from 200 µm to several centimeters, which excludes most taxa of rotifera and protozoa

181

zooplankton in freshwater ecosystems. Currently, ZOOSCAN systems are popularly applied

182

in marine ecosystems to rapidly estimate biomass and size distribution of mesozooplankton

183

for biodiversity monitoring (Schultes et al. 2009).

184

FlowCAM, which is developed based on the fluid imaging technology, is an automated

185

imaging flow cytometer. FlowCAM uses laser detection to capture digital images of

186

particles/organisms in a fluid. Image analysis of collected digitized images enable accurate

187

estimation of the abundance and size of organisms and automatic classification of organisms

188

(EÁlvarez et al. 2011). Depending on the setup of the instrument, FlowCAM can detect

189

organisms with body size from 3 to 3000 µm, including almost all species in zooplankton

190

communities in freshwater ecosystems, especially in polluted ones dominated by

191

smaller-sized species (Xiong et al. 2019). Though FlowCAM was created for analyzing

192

phytoplankton (Buskey & Hyatt 2006; Poulton 2016), more studies have used advanced 9

193

FlowCAM to quantitatively analyze zooplankton (Le Bourg et al. 2015; Di Mauro et al. 2016;

194

Broadway et al. 2018). Ide et al. (2008) carried out an adequate evaluation of zooplankton and

195

phytoplankton in feeding of two omnivory copepod species using FlowCAM. Stanislawczyk

196

(2016) demonstrated the capability of FlowCAM to distinguish between closely related

197

zooplankton taxa and detect rare species such as exotic species at early developmental stages.

198

Wang et al. (2017) used FlowCAM to quantitatively monitor zooplankton grazers in low

199

abundance in algal cultures for early warning. In addition, Wong et al. (2017) optimized

200

FlowCAM to overcome difficulties in low abundance samples. Altogether, the ability to

201

automatically detect and count zooplankton in mixed-species samples in a rapid way is an

202

obvious advantage in monitoring biodiversity in polluted freshwater ecosystems. In addition,

203

FlowCAM can detect low abundance species, such as threatened or newly introduced

204

nonnative invasive species, for conservation management and risk assessment.

205

2.3 Technical challenges

206

Traditional freshwater biodiversity monitoring is often based on direct observation

207

methods, which are usually done by using an instrument to directly (e.g., microscope or SEM)

208

or indirectly (e.g., ZOOSCAN and FlowCAM) observe morphological features of

209

zooplankton organisms. There is no doubt that such methods cannot be totally replaced by

210

new strategies such as genetic methods, thus we should know technical issues that have not

211

been solved to possibly avoid errors.

212

By using SEM, the accuracy of traditional morphological approaches can be highly

213

improved (Naito et al. 2019). Obviously, the detailed features, particularly new taxonomic

214

keys, should be deeply investigated and validated by competent taxonomists. Such detailed 10

215

investigations can subside debates on taxonomy but are unlikely appropriate for large-scale

216

surveys for ecological studies, such as causes and consequences of environmental pollution in

217

freshwater ecosystems. ZOOSCAN and FlowCAM highly increase the efficiency of sample

218

processing by automating and digitalizing samples. However, both ZOOSCAN and

219

FlowCAM compromise resolution of species identification (Gorsky et al. 2010; EÁlvarez et

220

al. 2013). Images captured by ZOOSCAN or FlowCAM are usually identified to high

221

taxonomic levels such as genus or above (EÁlvarez et al. 2013; Detmer et al. 2019). In

222

addition, ZOOSCAN has sound performance for species with body size ranging from 200µm

223

to several centimeters. While in polluted freshwater, zooplankton communities are commonly

224

dominated by small organisms such as rotifers and ciliates (Xiong et al. 2017, 2019; Yang et

225

al. 2018). Meanwhile, libraries for taxonomy classification of digital images were constituted

226

from zooplankton species in oceans, thus great effort is needed to create reference libraries

227

derived from freshwater ecosystems. FlowCAM is more compatible to monitoring

228

zooplankton in polluted freshwater ecosystems, but it is also limited by the same problems as

229

ZOOSCAN (i.e., reference libraries). In addition, automatic classification of samples is

230

error-prone, as all types of objects, including artifacts, are encountered in collected

231

zooplankton samples (Culverhouse et al. 2006; EÁlvarez et al. 2011, 2013). As a result,

232

validation or data quality control must be done to avoid errors generated from automatic

233

processes.

234

3 DNA-based methods for zooplankton biodiversity monitoring

235

3.1 Biodiversity monitoring based on (meta)barcoding

236

To overcome the limitation of conventional morphology-based methods, improved 11

237

methods are urgently required for more rapid, sensitive and efficient detection of zooplankton

238

species in freshwater ecosystems. With the development of molecular biology and sequencing

239

technologies, DNA-based approaches provide opportunities for species identification and

240

biodiversity survey in complex communities (Kelly et al. 2014; Creer et al. 2016; Brown et al.

241

2015). Taxa can be distinguished by a specific short DNA sequence derived from several

242

evolutionarily conserved genes in genomes or organelles (i.e., DNA barcode, Hebert et al.

243

2003). Thus, DNA barcoding-based method is particularly powerful for identifying cryptic

244

species by PCR amplification and sequencing of the selected DNA barcodes, which can

245

potentially avoid artificial errors by traditional methods. Recently, technical advances of

246

high-throughput sequencing (HTS) have largely revolutionized DNA barcoding-based

247

methods, allowing rapid species identification of individuals across diverse ranges of taxa in

248

environmental samples simultaneously in a single effort (i.e., DNA metabarcoding, Deiner et

249

al. 2017).

250

When compared to traditional methods, DNA metabarcoding-based methods are

251

minimally invasive, faster, comprehensive, and time/cost-effective (Lim et al. 2016; Valentini

252

et al. 2016; Ji et al. 2013; Xiong et al. 2016a). As a result, DNA metabarcoding-based

253

methods have been popularly applied in the detection and bio-monitoring of biodiversity in a

254

wide range of taxonomic groups, such as sharks, fishes, amphibians, and insects in aquatic

255

ecosystems (Hanfling et al. 2016; Valentini et al. 2016; Bista et al. 2017; Boussarie et al.

256

2018). Shaw et al. (2017) compared fish communities detected by DNA metabarcoding with

257

that by conventional fyke netting in the North Para River (NPR) in South Australia, and their

258

results showed that DNA metabarcoding approach could detect 100% of the net-caught fish 12

259

species under the premise that the sampling strategies were appropriate. The detection

260

probability of amphibians and the number of detected fishes via DNA metabarcoding were

261

higher than that by traditional methods (Valentini et al. 2016), clearly indicating that DNA

262

metabarcoding has the potential to become an important approach in biodiversity monitoring

263

in freshwater ecosystem. In addition, Yang et al. (2017a) compared the detection capacity of

264

DNA metabarcoding with traditional morphology-based methods in studying the zooplankton

265

community structure in eutrophic Lake Tai Basin of China, and their results showed that the

266

species composition and biomass represented by sequence read counts obtained by DNA

267

metabarcoding were consistent with morphological data. Indeed, DNA metabarcoding-based

268

methods largely facilitated our understanding of biological consequences of environmental

269

pollutions in freshwater ecosystems. Li et al. (2018) detected multiple taxa in protist and

270

metazoan communities in the Yangtze River Delta using DNA metabarcoding, and they

271

revealed that the community structure and biodiversity changes were mainly driven by

272

nutrient levels. Xiong et al. (2017, 2019) illustrated that large-sized arthropods in slightly

273

polluted river segments were shifted to small-sized species such as rotifers and ciliates in

274

highly disturbed areas, clearly putting forward an important ecological mechanism that the

275

role of species sorting can override dispersal as the dominant driver in determining

276

community structure (i.e., fine-scale species sorting hypothesis). Therefore, DNA

277

metabarcoding-based methods offer a sensitive, fast and powerful strategy to investigate

278

biodiversity fluctuation dynamics and further demonstrate important ecological problems

279

caused by water pollution derived from human activities (Chain et al. 2016; Xiong et al.

280

2019). 13

281

The typical operational procedure of DNA metabarcoding technology for freshwater

282

biodiversity monitoring consists of the following five steps: sample collection, DNA

283

extraction, PCR amplification for selected genetic markers, sequencing and bioinformatics

284

analysis (Fig. 3). Here we briefly discuss the key technical issues that require attention to

285

avoid or decrease errors/bias during biodiversity assessment. It should be noted that despite

286

DNA metabarcoding method has been widely used in biodiversity research and monitoring

287

(Goldberg et al. 2016; Leese et al. 2016; Deiner et al. 2017), many technical issues have not

288

solved or well solved (Xiong et al. 2016). Thus, perfect and standardized protocols are still

289

largely needed (Xiong et al. 2016a; Majaneva et al. 2018).

290

3.1.1 Sample collection and DNA isolation/capture

291

There are two strategies of sampling zooplankton in biodiversity monitoring. One is

292

sampling bulk specimen samples (community DNA metabarcoding), by which zooplankton

293

can be quantitatively collected and enriched by filtering the same volume of water at each

294

sampling site using plankton nets (Yang et al. 2017; Xiong et al. 2017, 2019). The collected

295

samples can be directly preserved in anhydrous alcohol, or further filtered through 5-µm

296

microporous filter membranes, then stored at -20 ℃ for subsequent DNA extraction. The

297

other strategy is sampling environmental DNA samples (eDNA metabarcoding) (Hanfling et

298

al. 2016; Zhang et al. 2018). Environmental DNA (eDNA) is the suspended DNA traces from

299

skin cells, organelles, gametes or even extracellular DNA of all organisms living in aquatic

300

ecosystems (Deiner et al. 2017). Environmental DNA can be captured onto filter membranes

301

by filtering 1-2 L of water in field or laboratory and preserved in anhydrous alcohol. To date,

302

there is still a debate on the selection of filers and associated parameters including type, pore 14

303

size, pre-filtration and filter preservation strategy during the eDNA capture process. The most

304

commonly used filter for eDNA capture is 0.45-µm cellulose nitrate membrane, followed by

305

0.75/1.2-µm glass microfiber filers or polyethersulfone (Hanfling et al. 2016; Majaneva et al.

306

2018). Besides, in order to avoid false negatives caused by random sampling of rare taxa, it is

307

important to conduct repeated sampling for at least three times at each sampling site

308

(Thomsen et al. 2012).

309

It is important to keep in mind when we collect eDNA samples for biodiversity

310

monitoring that 1) the stability, dispersal and degradation rate of eDNA are substantially

311

influenced by various environmental factors such as temperature, pH, light intensity and

312

water chemistry (Goldberg et al. 2016; Seymour et al. 2018); 2) the temporal and spatial

313

distribution of eDNA highly vary in different ecosystems such as rivers and lakes, largely

314

owing to the transport of eDNA over large geographical scales in rivers and long retention

315

time in lakes (Turner et al. 2015; Deiner et al. 2016; Carraro et al. 2018); 3) eDNA from

316

different vertical depth of river and lakes has different biological significance, with that of

317

surface water indicating recent site biodiversity while sediments representing historical

318

biodiversity (Deiner et al. 2017). Thus, steps of eDNA capture need to be optimized in each

319

study, especially for highly polluted water with special hydrochemical features.

320

Currently, most studies relating to zooplankton biodiversity analysis used bulk specimen

321

samples (Xiong et al. 2017, 2019; Chain et al. 2015). However, when using eDNA to perform

322

biodiversity monitoring, sampling will be easier and less invasive. Djurhuus et al. (2018)

323

assessed the potential of eDNA metabarcoding to monitoring zooplankton biodiversity, by

324

comparing method of eDNA metabarcoding to bulk sample metabarcoding and 15

325

morphological methods, and they suggested that eDNA metabarcoding should be able to

326

provide complementary insights in biodiversity monitoring of zooplankton.

327

3.1.2 Selection of robust genetic markers and universal primers

328

“DNA metabarcode” is a fragment of DNA sequence being used for taxonomic

329

identification of multiple species existing in a mixed sample collected from communities or

330

environments (Deiner et al. 2017). The selection of DNA metabarcode will greatly influence

331

the detected species number and composition of taxonomic groups, as well as the accuracy of

332

species identification (Andújar et al. 2018a). Thus, the choice of appropriate genetic makers

333

is of vital importance for DNA metabarcoding analysis (Zhan & MacIsaac 2015). A desired

334

genetic marker for PCR primer design is expected to simultaneously possess both

335

evolutionarily conserved and hypervariable regions, of which conserved regions were used to

336

design universal primers for amplifying a wide range of species or taxonomic groups

337

(versatility), and hypervariable regions in the amplicons were used to accurately distinguish

338

the closely related species (high resolution) (Zhan & MacIsaac 2015).

339

Owing to the relatively high evolutionary rate and high taxonomic resolution capability,

340

the mitochondrial cytochrome c oxidase subunit I gene (COI) is considered as a competent

341

candidate genetic marker for metabarcoding survey for metazoa (Zhan & Maclsaac 2015;

342

Andújar et al. 2018b; Fernández et al. 2018). However, the amplification of COI in some

343

taxonomic groups such as crustaceans is still a difficult task, particularly in bulk samples (see

344

the review by Zhan & Maclsaac 2015 and references therein). To find a desired genetic

345

marker to metabarcode zooplankton biodiversity, Zhan et al. (2013, 2014) designed primer

346

pairs based on 18S and compared three commonly used genetic marker, including 18S, COI 16

347

and 16S by testing performance of different primer pairs, and they proposed that 18S was the

348

most efficient and powerful genetic marker for zooplankton biodiversity monitoring. Clarke

349

et al. (2017) claimed that COI (Leray et al. 2013) and 18S (Zhan et al. 2013) recovered

350

similar taxonomic coverage and beta-diversity, but different biomass and sequence reads

351

relationship and resolution, by comparing performance of these two best-known primer sets

352

in biodiversity study of zooplankton. Yet, Andújar et al. (2018b) argued that COI still should

353

be the most appropriate DNA metabarcode for bulk community analysis based on the

354

following reasons: 1) high resolution for taxonomic identification; 2) extensive coverage of

355

COI in reference sequence databases; 3) advantage of a protein-coding gene to identify

356

spurious sequences.

357

For eDNA metabarcoding, there are more challenges for selecting genetic markers and

358

primers, mainly due to low quality and quantity of eDNA and high proportion of

359

co-amplification of microbial DNA (Stat et al. 2017). Here, we urgently suggest that genetic

360

markers and associated universal primers should be well tested before application,

361

particularly on several parameters such as coverage of taxonomic groups and amplification

362

efficiency and bias. It is true that there are no perfect genetic markers and associated

363

universal primers for all taxonomic groups, but careful consideration of all influential

364

parameters can largely improve the power of DNA metabarcoding-based biodiversity survey.

365

While researchers struggle to select efficient genetic marker and primer sets in

366

metabarcoding, PCR-free methods are emerging recently, such as shotgun sequencing after

367

mitochondrial enrichment (Zhou et al. 2013). This method is independent on PCR, as

368

mitochondrial DNA is enriched during metaDNA extraction and sequenced by ultra-deep 17

369

high-throughput sequencing (HTS), and then COI sequences are retrieved by bioinformatic

370

analyses (Zhou et al. 2013). To avoid high cost of ultra-deep HTS and advance PCR-free

371

methods in routine environmental biomonitoring, a gene-enrichment technique was

372

recommended by Dowle et al. (2016). This method is derived from probe enrichment

373

techniques used in human genomic research (Blow 2009) and uses gene capture probes to

374

enrich target genes, followed by HTS to monitor biodiversity of aquatic communities (Dowle

375

et al. 2016). These two methods overcome PCR-derived biases to provide better estimate of

376

species abundance or biomass of target organisms, enhancing both the DNA sequence

377

information per taxon and the number of taxa.

378

3.1.3 Bioinformatics analysis

379

Enormous short sequence reads are generated by high throughput sequencing (HTS) in

380

DNA metabarcoding analysis, particularly after processing a large number of samples

381

collected from large geographical scales. Such big data sets pose vast challenges to

382

subsequent data processing procedures. A general data analysis process includes raw data

383

filtering, clustering of operational taxonomic units (OTUs), and taxonomy assignment.

384

Available evidence shows that sequencing errors greatly influence the biodiversity

385

estimates (Xiong et al. 2016a; Kunin et al. 2010). Thus, the removal of sequencing errors is

386

the first and one of the most important steps, which is challenged by discerning real rare taxa

387

from sequencing errors. Both rare species and artificial reads are usually recovered by low

388

number of sequences (e.g., low abundance OTUs), and the process of removal artificial reads

389

is prone to filter out a large proportion of rare species in samples (Zhan et al. 2014).

390

Depending on research aims, the clustering strategy for OTUs is another essential step for 18

391

assessing community biodiversity using DNA metabarcoding. These clustered molecular

392

OTUs were assigned to traditional taxonomic species in biodiversity monitoring, while

393

predefined similarity threshold (such as 97%) used in the clustering step may split one

394

traditional species into two or more OTUs or sequences for different taxa were clustered into

395

the same OTUs. To resolve this issue, newly developed methods, such as DADA2 (Callahan

396

et al. 2017) and UNOISE3 (Edgar 2016) were advanced algorithms of discerning and

397

removing sequencing errors and picked OTUs at similarity threshold of 100%, and LULU

398

was created for removing erroneous OTUs (Frøslev et al. 2017). Xiong & Zhan (2018) tested

399

different clustering strategies in studying zooplankton diversity, including newly developed

400

non-clustering (DADA2 and UNOISE 3) and clustering with different thresholds. Their

401

results showed that largely varied alpha biodiversity estimates were obtained by different

402

clustering strategies, but the ecological conclusions were consistently drawn by

403

non-clustering and clustering thresholds of 97-99%.

404

3.2 Indicator species detection based on metabarcoding

405

Owing to high sensitivity to environmental stressors, biological indicator species play

406

crucial roles in assessing pollution effects in aquatic ecosystems and provide early warning of

407

environmental changes (Casazza et al. 2002; Xiong et al. 2017, 2019). Identifying indicator

408

species, rather than traditional qualitative analysis of all species in biological communities,

409

provides a convenient and appealing approach in environmental monitoring, conservation and

410

management (Mächler et al. 2014). DNA barcoding has enhanced the sensitivity and

411

efficiency of species-level identification and highlights its wide use in indicator species

412

detection in biodiversity monitoring efforts (Hebert et al. 2003; Hebert & Gregory 2005). A

413

number of studies have documented the utility of DNA barcoding for species identification,

19

414

such as fish (Ward et al. 2005), copepods (Blanco-Bercial et al. 2014), crustaceans

415

(Radulovici et al. 2009), and molluscs (Layton et al. 2014). Recently, the application of DNA

416

barcoding method has been extended dramatically owing to the high-throughput sequencing

417

(HTS), enabling the identification of indicator species from mixed species samples or

418

communities (Cristescu 2014; Taberlet et al. 2012). Using HTS technologies to study the

419

relationship between geographical distributions of bacterial communities and water pollution,

420

Yang et al. (2019) revealed several effective bacteria as bio-indicators to assess river

421

pollution in Songhua River, suggesting the potential of micro-organisms as useful biological

422

indicators. Metabarcoding-based methods have been used to identify a variety of aquatic

423

indicator species for water-quality assessment and biodiversity monitoring, such as

424

arthropods (MäcHler et al. 2014; Thomsen et al. 2012; Xiong et al. 2017), gastropods

425

(Goldberg et al. 2013), amphibians (Goldberg et al. 2011; Thomsen et al. 2012), reptiles

426

(Piaggio et al. 2013), fishes (Jerde et al. 2011; Wilcox et al. 2013), and mammals (Foote et al.

427

2012; MäcHler et al. 2014). Thus, metabarcoding-based methods are robust tools to identify

428

indicative species from microscopic zooplankton.

429

3.3 Rare species detection based on metabarcoding and qPCR

430

Typically, aquatic communities are composed of a few dominant species and a high

431

diversity of low abundance species (i.e., ‘‘rare biosphere’’, Lynch & Neufeld 2015; Sogin et

432

al. 2006). The rare biosphere taxa may switch to new abundant members of a community in

433

response to environmental perturbation or change, and thus could provide a potential resource

434

of genetic and functional diversity to maintain ecosystem functioning. The rare biosphere in

435

running water ecosystems mainly include two types of species: native rare species and newly

436

introduced non-indigenous species (NIS). Owing to environmental pollution or demographic

437

stochasticity, native rare species may be at risk of extinction and become endangered (Wilson 20

438

et al. 2011). In addition, newly introduced NIS generally maintain low population density in

439

communities for a long period of time, even in cases when they eventually become dominant

440

to cause large-scale negative effects (Crooks & Soule 1999; Zhan et al. 2013). Therefore,

441

novel methods and effective measures should be taken to explore and monitor the rare

442

biosphere, in particular to identify native threatened rare species for conservation and

443

recently introduced NIS for biosecurity. However, there remain major technical challenges to

444

detect rare species, especially for microscopic organisms in freshwater ecosystems, owing to

445

their tiny body size, diverse species, complex community structure, and ambiguous

446

morphological traits (McDonald 2004; Zhan & MacIsaac 2015).

447

Metabarcoding has provided effective access to investigating rare species in complex

448

aquatic communities (Creer 2010; Xiong et al. 2016a). Zhan et al. (2013) quantitatively tested

449

the sensitivity of metabarcoding and claimed that metabarcoding enabled the detection of rare

450

species in complex zooplankton communities (biomass as low as 2.3 × 10-5%). Mounting

451

studies highlight the crucial role of metabarcoding in monitoring endangered species for

452

conservation (see the review by Cristescu & Hebert 2018 and references therein). Meanwhile,

453

metabarcoding helps early detection of NIS in aquatic ecosystems. For instance, Brown et al.

454

(2016) detected 24 non-indigenous species in Canadian ports, 11 of which were firstly

455

reported in the locations. Westfall et al. (2019) proposed a metabarcoding-based new

456

approach to molecular biosurveillance of invasive zooplankton species. Besides, for

457

accurately identifying protected species, DNA metabarcoding-based methods can provide

458

useful information about speciation time and phylogenetic diversity to perform quantitative

459

comparison rather than simply depending on the number of species within this area (Pečnikar 21

460

& Buzan 2014). In addition, commonly used genetic markers for HTS, such as COI, mt16S,

461

18S rDNA, would largely increase the comparability among different species, allowing us to

462

make the quantitative measurement of genetic diversity and evolutionary potential of rare

463

species and to confirm the relationships between endangered species and their surrounding

464

species. Thus, metabarcoding provides important insights in rare species detection, which

465

allows managers to make scientific decisions of species, populations and individuals for

466

protection.

467

Except screening rare species in mixed complex communities using metabarcoding,

468

species-specific PCR methods, including conventional PCR (cPCR) and real-time

469

quantitative PCR (qPCR), are DNA-based approaches to directly detect targeted endangered

470

or NIS species in low abundance (Xia et al. 2018). Conventional PCR has been applied to the

471

detection of a variety of aquatic NIS, including amphibians (Rana catesbeiana, Ficetola et al.

472

2008), crustaceans (Procambarus clarkii, Tréguier et al. 2014), mollusca (Limnoperna

473

fortune, Xia et al. 2018), and many other taxa. Real-time quantitative PCR (qPCR), which is

474

usually considered to be more sensitive than cPCR, provides a promising molecular tool for

475

detection and quantification of rare species (Arya et al. 2005; Wilcox et al. 2013). For

476

example, a quantitative real-time PCR (qPCR) amplification of 16s rDNA was performed to

477

quantitatively assess harmful algal bloom (HAB) risks in the Great Lakes (Doblin et al. 2007).

478

Recently, novel solutions, such as carbon nanotube platforms (Mahon et al. 2011), nucleic

479

acid sequence-based amplification (NASBA, Fykse et al. 2012), and droplet digital PCR

480

(ddPCR, Nathan et al. 2014), have extended the use of DNA-based methods in monitoring

481

targeted rare species. 22

482 483

3.4 Technical difficulties and possible solutions Although DNA-based methods exhibit evident advantages in assessing biodiversity,

484

there remain technical difficulties and challenges that need to be addressed for the

485

implementation of biodiversity monitoring programs. As mentioned above, PCR-free

486

approaches are able to overcome some limitations of PCR-based methods. However, these

487

methods have not been well tested in complex real-community samples (Zhou et al. 2013)

488

and showed low sensitivity to detect rare species (Srivathsan et al. 2014; Zhan and MacIsaac,

489

2015). In this study, we focus on the commonly used PCR-based methods (i.e., DNA

490

metabarcoding, Fig. 4).

491

3.4.1 False positives and negatives

492

The most notable problem confronting the process of DNA metabarcoding analyses is

493

the risk of error sources, including false positives (species detected but absent) and false

494

negatives (species undetected but present). A variety of issues can lead to false positives and

495

negatives (Darling & Mahon 2011; Zhan & MacIsaac 2015; Xiong et al. 2016a).

496

False positives, which is also known as Type II errors, can result from

497

cross-contamination from multiple sources, such as improper handling of samples, inadequate

498

specificity primers, and errors in data analysis (Xiong et al. 2016a; Cristescu et al. 2018; Li et

499

al. 2018). As a result of the increasing throughput of sequencing technologies, the pooling of

500

samples may lead to cross-contamination (Schmieder & Edwards 2011). Careful

501

manipulation, standard experimental protocols and contamination removal pipelines may

502

reduce cross-contamination risks and false positives. Moreover, negative and positive

503

controls should be added in the entire process (Jerde et al. 2011; Rees et al. 2014; Thomsen & 23

504

Willerslev 2015; Taberlet et al. 2018). In addition to cross-contamination, tag jumping among

505

pooled samples is another reason for false positives (Carlsen et al. 2012; Xiong et al. 2016a;

506

Zhan et al. 2016). There are some causes of tag jumping for HTS-based studies, such as

507

remaining unused tagged primers, using only one tagged primer, or sequencing long

508

amplicons (Xiong et al. 2016a). To minimize the probability of false positives caused by tag

509

jumping, paired tags should be added at both sides of amplicons (Carlsen et al. 2012).

510

Increasing the number of biological replicates, adding negative controls and applying strict

511

filtering pipelines would provide effective approaches to reducing the effect of tag jumping

512

(Kumar et al. 2011). In addition, other approaches that can contribute to avoidance of tag

513

jumping includes carefully purifying PCR products, immediately freezing PCR products, and

514

shortening their storage time (Kumar et al. 2011; Xiong et al. 2016).

515

The sources of false negatives (Type I errors) are mainly derived from random sampling

516

and biased PCR amplification (Xiong et al. 2016; Zhan et al. 2016a). When investigating

517

community structure with DNA metabarcoding, random sampling processes can result in low

518

reproducibility among replicate samples, especially for rare species, thus leading to false

519

negatives (Clarke et al. 2014; Engelbrektson et al. 2010; Zhan et al. 2014). Several technical

520

approaches should be performed to decrease false negatives by using more biological and

521

technical replicates, deeper sequencing, primers blocking against dominant taxa and

522

optimized species occupancy models (MacKenzie et al. 2002; Xiong et al. 2016a; Zhan et al.

523

2016). Another common and severe problem influencing taxonomic resolution power in DNA

524

metabarcoding studies is PCR amplification biases among species (Xiong et al. 2016). This

525

error source can be introduced by many factors, such as universality of the primers, annealing 24

526

temperature, the number of replication cycles, and relative abundance of different species in a

527

community (Bellemain et al. 2010; Engelbrektson et al. 2010). PCR amplification bias is

528

strongly influenced by the annealing temperature and the number of replication cycles during

529

library preparation. Low annealing temperature and cycle number can reduce the bias (Ishii &

530

Fukui 2001). Additional efforts include optimizing conditions of PCR amplification by

531

increasing template concentrations and adopting wise primers (Lim et al. 2010; Shokralla et

532

al. 2012). More importantly, the utilization of multiple versatile primers or two-step nested

533

PCR would reduce amplification biases and enhance the opportunity of rare species detection

534

(Sun et al. 2015; Xiong et al. 2016a; Zhang et al. 2018).

535

3.4.2 Reference database

536

Retrieving the taxonomic composition from sequences data is one of the primary aims

537

when we monitor biodiversity using DNA-based methods. This step is completely dependent

538

on comparing sequences against a metabarcode reference dataset containing taxonomy

539

information (Taberlet et al. 2018). The high-quality and complete reference databases for

540

taxonomic assignment directly determine the reliability of DNA metabarcoding. Even though

541

a large number of DNA sequences has been submitted to public databases, unbalanced

542

representation of different taxonomic groups in these public sequence databases is a major

543

issue (Briski et al. 2011). Available sequences for poorly studied species in public databases

544

remain low when compared to commercially important or better studied species (Briski et al.

545

2011). Thus, broad collaborations and sharing among different research groups who

546

particularly have their own taxa of interest, should be made to develop and improve

547

community-level reference libraries (Valentini et al. 2016). Additionally, most sequence 25

548

information was obtained from traditional molecular makers, such as COI and ribosomal

549

RNA genes in these databases, which is not enough to identify complex communities and

550

closely related species. Thus, more sequence information with diverse molecular makers

551

should collect in order to build comprehensive reference libraries.

552

From a taxonomic point view, reference datasets (Table 1) are poorly connected, though

553

part of them, such as EMBL, GenBank and DDBJ, have been united into the International

554

Nucleotides Sequence Database Collaboration (INSDC) and synchronized daily (Taberlet et

555

al. 2018). Lacking coherence among datasets largely limits meta-analyses on

556

metabarcoding-based studies. Thus, collaborative efforts remain necessary in the future for

557

biodiversity monitoring at the global scale.

558

4 Future perspectives

559

In summary, metabarcoding is revolutionizing the investigation of freshwater

560

biodiversity and presents a powerful tool of detection of “hidden diversity” under water

561

surface (Lindeque et al. 2013). We propose that DNA-based metabarcoding should be

562

embraced by researchers and managers, as its inherent practicable and applicable features can

563

largely solve technical issues in biodiversity assessment in polluted freshwater ecosystems.

564

Indeed, DNA-based metabarcoding has been approved by several international organizations,

565

such as the European Water Framework Directive of European Union. Yet, many technical

566

issues still remain in the application of metabarcoding-based biomonitoring in freshwater

567

ecosystems. We should firstly continue the development of taxonomically comprehensive

568

reference databases based on multiple genetic markers to support taxonomic retrieval of

569

metabarcoding analyses. Additionally, we should screen more variables gene regions, which 26

570

can ensure correct identification, discrimination and detection of closely related or cryptic

571

species. Alternatively, as HTS becomes more accessible and less expensive, the use of

572

short-gun sequencing strategy to sequence meta-DNA extracted from bulk samples would

573

increase the resolution of taxonomical identification. In addition, shot-gun sequencing is a

574

PCR-free method, which is beneficial to quantification of taxon abundance. Spiking the

575

standard DNA into different samples is a good method to make advance of quantification in

576

metabarcoding analyses. Finally, there is no doubt that traditional morphology-based methods

577

cannot be discarded, although many technical issues still exist among morphology-based

578

methods. Both traditional morphology-based and DNA-based methods must be

579

cross-referenced to ensure accurate and rapid identification of zooplankton species, and to

580

promote the dissection of causes and consequences of biodiversity loss in polluted freshwater

581

ecosystems.

582

Author Contributions

583

A.Z., B.S. and W.X. conceived the study. All authors wrote and revised the manuscript.

584

Acknowledgments

585

This work was supported by the National Natural Science Foundation of China [grant

586

numbers 31800307, 31572228], National Key R&D Program of China [grant number

587

2016YFC0500406], and Chinese Academy of Science [grant number ZDRW-ZS-2016-5-6].

588

References

589

Alahuhta, J., Erős, T., Kärnä, O. M., Soininen, J., Wang, J., & Heino, J. (2019).

590

Understanding environmental change through the lens of trait-based, functional, and

591

phylogenetic biodiversity in freshwater ecosystems. Environmental Reviews, 27(2), 27

592 593

263-273. Andújar, C., Arribas, P., Gray, C., Bruce, C., Woodward, G., Yu, D. W., & Vogler, A. P.

594

(2018a). Metabarcoding of freshwater invertebrates to detect the effects of a pesticide

595

spill. Molecular Ecology, 27(1), 146-166.

596

Andújar, C., Arribas, P., Yu, D. W., Vogler, A. P., & Emerson, B. C. (2018b). Why the COI

597

barcode should be the community DNA metabarcode for the metazoa. Molecular

598

Ecology, 27(20), 3968-3975.

599

Arya, M., Shergill, I. S., Williamson, M., Gommersall, L., Arya, N., & Patel, H. R. (2005).

600

Basic principles of real-time quantitative PCR. Expert Review of Molecular Diagnostics,

601

5(2), 209-219.

602

Azevêdo, D. J. S., Barbosa, J. E. L., Gomes, W. I. A., Porto, D. E., Marques, J. C., & Molozzi,

603

J. (2015). Diversity measures in macroinvertebrate and zooplankton communities related

604

to the trophic status of subtropical reservoirs: Contradictory or complementary

605

responses?. Ecological Indicators, 50, 135-149.

606

Bellemain, E., Carlsen, T., Brochmann, C., Coissac, E., Taberlet, P., & Kauserud, H. (2010).

607

ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential

608

PCR biases. BMC Microbiology, 10(1), 189.

609

Bista, I., Carvalho, G. R., Walsh, K., Seymour, M., Hajibabaei, M., Lallias, D., ... & Creer, S.

610

(2017). Annual time-series analysis of aqueous eDNA reveals ecologically relevant

611

dynamics of lake ecosystem biodiversity. Nature communications, 8, 14087.

612 613

Blackman, R. C., Mächler, E., Altermatt, F., Arnold, A., Beja, P., Boets, P., ... & Macher, J. (2019). Advancing the use of molecular methods for routine freshwater 28

614

macroinvertebrate biomonitoring–the need for calibration experiments. Metabarcoding

615

and Metagenomics, 3, 49-57.

616

Blanco-Bercial, L., Cornils, A., Copley, N., & Bucklin, A. (2014). DNA barcoding of marine

617

copepods: assessment of analytical approaches to species identification. PLoS currents,

618

6.

619

Boussarie, G., Bakker, J., Wangensteen, O. S., Mariani, S., Bonnin, L., Juhel, J. B., ... &

620

Vigliola, L. (2018). Environmental DNA illuminates the dark diversity of sharks.

621

Science advances, 4(5), eaap9661.

622

Briski, E., Cristescu, M. E., Bailey, S. A., & MacIsaac, H. J. (2011). Use of DNA barcoding

623

to detect invertebrate invasive species from diapausing eggs. Biological Invasions, 13(6),

624

1325-1340.

625

Broadway, K., Detmer, T., Potter, C., Collins, S., Parkos, J. III., & Wahl, D. H. (2018). Go

626

with the Flow? Strengths and Weaknesses of FlowCAM? for Characterizing

627

Zooplankton Communities. Paper presented at 2018 Midwest Fish and Wildlife

628

Conference, Milwaukee, United States.

629

Brown, E. A., Chain, F. J., Crease, T. J., MacIsaac, H. J., & Cristescu, M. E. (2015).

630

Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably

631

describe zooplankton communities?. Ecology and Evolution, 5(11), 2234-2251.

632

Brown, E. A., Chain, F. J., Zhan, A., MacIsaac, H. J., & Cristescu, M. E. (2016). Early

633

detection of aquatic invaders using metabarcoding reveals a high number of non‐

634

indigenous species in C anadian ports. Diversity and Distributions, 22(10), 1045-1059.

635

Bucklin, A., Lindeque, P. K., Rodriguez-Ezpeleta, N., Albaina, A., & Lehtiniemi, M. (2016). 29

636

Metabarcoding of marine zooplankton: prospects, progress and pitfalls. Journal of

637

Plankton Research, 38(3), 393-400.

638

Bush, A., Sollmann, R., Wilting, A., Bohmann, K., Cole, B., Balzter, H., ... & Dawson, T. P.

639

(2017). Connecting Earth observation to high-throughput biodiversity data. Nature

640

Ecology & Evolution, 1(7), 0176.

641

Buskey, E. J., & Hyatt, C. J. (2006). Use of the FlowCAM for semi-automated recognition

642

and enumeration of red tide cells (Karenia brevis) in natural plankton samples. Harmful

643

Algae, 5(6), 685-692.

644

Butchart, S. H., Walpole, M., Collen, B., Van Strien, A., Scharlemann, J. P., Almond, R. E., ...

645

& Carpenter, K. E. (2010). Global biodiversity: indicators of recent

646

declines. Science, 328(5982), 1164-1168.

647

Callahan, B. J., McMurdie, P. J., & Holmes, S. P. (2017). Exact sequence variants should

648

replace operational taxonomic units in marker-gene data analysis. The ISME journal,

649

11(12), 2639-2643.

650

Carlsen, T., Aas, A. B., Lindner, D., Vrålstad, T., Schumacher, T., & Kauserud, H. (2012).

651

Don't make a mista (g) ke: is tag switching an overlooked source of error in amplicon

652

pyrosequencing studies?. Fungal Ecology, 5(6), 747-749.

653

Carraro, L., Hartikainen, H., Jokela, J., Bertuzzo, E., & Rinaldo, A. (2018). Estimating

654

species distribution and abundance in river networks using environmental DNA.

655

Proceedings of the National Academy of Sciences, 115(46), 11724-11729.

656

Casazza, G., Silvestri, C., & Spada, E. (2002). The use of bio-indicators for quality

657

assessments of the marine environment: Examples from the Mediterranean Sea. Journal 30

658 659 660 661

of Coastal Conservation, 8(2), 147. Cazzolla Gatti, R. (2016). Freshwater biodiversity: A review of local and global threats. International Journal of Environmental Studies, 73(6), 887-904. Chain, F. J., Brown, E. A., MacIsaac, H. J., & Cristescu, M. E. (2016). Metabarcoding reveals

662

strong spatial structure and temporal turnover of zooplankton communities among

663

marine and freshwater ports. Diversity and Distributions, 22(5), 493-504.

664

Clarke, L. J., Beard, J. M., Swadling, K. M., & Deagle, B. E. (2017). Effect of marker choice

665

and thermal cycling protocol on zooplankton DNA metabarcoding studies. Ecology and

666

Evolution, 7(3), 873-883.

667

Clarke, L. J., Soubrier, J., Weyrich, L. S., & Cooper, A. (2014). Environmental metabarcodes

668

for insects: in silico PCR reveals potential for taxonomic bias. Molecular ecology

669

resources, 14(6), 1160-1170.

670

Clausen, R., & York, R. (2008). Global biodiversity decline of marine and freshwater fish: a

671

cross-national analysis of economic, demographic, and ecological influences. Social

672

Science Research, 37(4), 1310-1320.

673

Creer, S., Deiner, K., Frey, S., Porazinska, D., Taberlet, P., Thomas, W. K., ... & Bik, H. M.

674

(2016). The ecologist's field guide to sequence-based identification of biodiversity.

675

Methods in Ecology and Evolution, 7(9), 1008-1018.

676

Creer, S., Fonseca, V. G., Porazinska, D. L., Giblin‐Davis, R. M., Sung, W., Power, D. M., ...

677

& Thomas, W. K. (2010). Ultrasequencing of the meiofaunal biosphere: practice, pitfalls

678

and promises. Molecular Ecology, 19, 4-20.

679

Cristescu, M. E. (2014). From barcoding single individuals to metabarcoding biological 31

680

communities: towards an integrative approach to the study of global biodiversity. Trends

681

in Ecology & Evolution, 29(10), 566-571.

682

Cristescu, M. E., & Hebert, P. D. (2018). Uses and misuses of environmental DNA in

683

biodiversity science and conservation. Annual Review of Ecology, Evolution, and

684

Systematics, 49, 209-230.

685

Culverhouse, P. F., Williams, R., Benfield, M., Flood, P. R., Sell, A. F., Mazzocchi, M. G., ...

686

& Sieracki, M. (2006). Automatic image analysis of plankton: future perspectives.

687

Marine Ecology Progress Series, 312, 297-309.

688

Darling, J. A., & Mahon, A. R. (2011). From molecules to management: adopting DNA-based

689

methods for monitoring biological invasions in aquatic environments. Environmental

690

Research, 111(7), 978-988.

691

Darwall, W., Smith, K., Allen, D., Seddon, M., Reid, G. M., Clausnitzer, V., & Kalkman, V. J.

692

(2009). Freshwater biodiversity: a hidden resource under threat. Wildlife in a changing

693

world–An Analysis of the 2008 IUCN Red List of Threatened Species, 43.

694

De Vargas, C., Audic, S., Henry, N., Decelle, J., Mahé, F., Logares, R., ... & Carmichael, M.

695

(2015). Eukaryotic plankton diversity in the sunlit ocean. Science, 348(6237), 1261605.

696

Deiner, K., Bik, H. M., Mächler, E., Seymour, M., Lacoursière‐Roussel, A., Altermatt, F., ...

697

& Pfrender, M. E. (2017). Environmental DNA metabarcoding: Transforming how we

698

survey animal and plant communities. Molecular Ecology, 26(21), 5872-5895.

699

Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J. C., & Altermatt, F. (2016).

700

Environmental DNA reveals that rivers are conveyer belts of biodiversity information.

701

Nature communications, 7, 12544. 32

702

Detmer, T. M., Broadway, K. J., Potter, C. G., Collins, S. F., Parkos, J. J., & Wahl, D. H.

703

(2019). Comparison of microscopy to a semi-automated method (FlowCAM?) for

704

characterization of individual-, population-, and community-level measurements of

705

zooplankton. Hydrobiologia, 838(1), 99-110.

706

Di Mauro, R., Kupchik, M. J., & Benfield, M. C. (2016). Assessment of the application and

707

efficacy of the FlowCam for zooplankton analysis. In American Geophysical Union,

708

Ocean Sciences Meeting 2016, abstract# IS34A-2324.

709

Djurhuus, A., Pitz, K., Sawaya, N. A., Rojas‐Márquez, J., Michaud, B., Montes, E., ... &

710

Breitbart, M. (2018). Evaluation of marine zooplankton community structure through

711

environmental DNA metabarcoding. Limnology and Oceanography: Methods, 16(4),

712

209-221.

713

Doblin, M. A., Coyne, K. J., Rinta-Kanto, J. M., Wilhelm, S. W., & Dobbs, F. C. (2007).

714

Dynamics and short-term survival of toxic cyanobacteria species in ballast water from

715

NOBOB vessels transiting the Great Lakes—implications for HAB invasions. Harmful

716

Algae, 6(4), 519-530.

717

Dowle, E. J., Pochon, X., C. Banks, J., Shearer, K., & Wood, S. A. (2016). Targeted gene

718

enrichment and high‐throughput sequencing for environmental biomonitoring: a case

719

study using freshwater macroinvertebrates. Molecular Ecology Resources, 16(5),

720

1240-1254.

721

Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z. I., Knowler, D. J., Lévêque,

722

C., ... & Sullivan, C. A. (2006). Freshwater biodiversity: importance, threats, status and

723

conservation challenges. Biological Reviews, 81(2), 163-182. 33

724

Duggan, I. C., Green, J. D., & Shiel, R. J. (2001). Distribution of rotifers in North Island,

725

New Zealand, and their potential use as bioindicators of lake trophic state. In Rotifera IX

726

(pp. 155-164). Springer, Dordrecht.

727

EÁlvarez, E., López-Urrutia, Á., Nogueira, E., & Fraga, S. (2011). How to effectively sample

728

the plankton size spectrum? A case study using FlowCAM. Journal of Plankton

729

Research, 33(7), 1119-1133.

730

EÁlvarez, E., Moyano, M., López-Urrutia, Á., Nogueira, E., & Scharek, R. (2013). Routine

731

determination of plankton community composition and size structure: a comparison

732

between FlowCAM and light microscopy. Journal of Plankton Research, 36(1), 170-184.

733 734 735

Edgar, R. C. (2016). UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv, 081257. Engelbrektson, A., Kunin, V., Wrighton, K. C., Zvenigorodsky, N., Chen, F., Ochman, H., &

736

Hugenholtz, P. (2010). Experimental factors affecting PCR-based estimates of microbial

737

species richness and evenness. The ISME journal, 4(5), 642.

738

Fernández S, Rodríguez S, Martínez JL, Borrell YJ, Ardura A, García-Vázquez E (2018)

739

Evaluating freshwater macroinvertebrates from eDNA metabarcoding: A river Nalón

740

case study. PLoS ONE, 13, e0201741.

741 742 743

Ficetola, G. F., Miaud, C., Pompanon, F., & Taberlet, P. (2008). Species detection using environmental DNA from water samples. Biology letters, 4(4), 423-425. Foote, A. D., Thomsen, P. F., Sveegaard, S., Wahlberg, M., Kielgast, J., Kyhn, L. A., ... &

744

Gilbert, M. T. P. (2012). Investigating the potential use of environmental DNA (eDNA)

745

for genetic monitoring of marine mammals. PloS one, 7(8), e41781. 34

746

Freyhof, J., & Brooks, E. (2017). European red list of freshwater fishes.

747

Frøslev, T. G., Kj?ller, R., Bruun, H. H., Ejrn?s, R., Brunbjerg, A. K., Pietroni, C., & Hansen,

748

A. J. (2017). Algorithm for post-clustering curation of DNA amplicon data yields

749

reliable biodiversity estimates. Nature communications, 8(1), 1188.

750

Fykse, E. M., Nilsen, T., Nielsen, A. D., Tryland, I., Delacroix, S., & Blatny, J. M. (2012).

751

Real-time PCR and NASBA for rapid and sensitive detection of Vibrio cholerae in

752

ballast water. Marine pollution bulletin, 64(2), 200-206.

753 754 755 756

Garric, J. (2013). Emerging issues in ecotoxicology: pharmaceuticals and personal care products (PPCPs). Encyclopedia of aquatic ecotoxicology, 407-428. Geist, J. (2011). Integrative freshwater ecology and biodiversity conservation. Ecological Indicators, 11(6), 1507-1516.

757

Goldberg, C. S., Pilliod, D. S., Arkle, R. S., & Waits, L. P. (2011). Molecular detection of

758

vertebrates in stream water: a demonstration using Rocky Mountain tailed frogs and

759

Idaho giant salamanders. PloS one, 6(7), e22746.

760

Goldberg, C. S., Sepulveda, A., Ray, A., Baumgardt, J., & Waits, L. P. (2013). Environmental

761

DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus

762

antipodarum). Freshwater Science, 32(3), 792-800.

763

Goldberg, C. S., Turner, C. R., Deiner, K., Klymus, K. E., Thomsen, P. F., Murphy, M. A., ...

764

& Laramie, M. B. (2016). Critical considerations for the application of environmental

765

DNA methods to detect aquatic species. Methods in Ecology and Evolution, 7(11),

766

1299-1307.

767

Gorsky, G., Ohman, M. D., Picheral, M., Gasparini, S., Stemmann, L., Romagnan, J. B., ... & 35

768

Prejger, F. (2010). Digital zooplankton image analysis using the ZooScan integrated

769

system. Journal of Plankton Research, 32(3), 285-303.

770

Green, P. A., Vörösmarty, C. J., Harrison, I., Farrell, T., Sáenz, L., & Fekete, B. M. (2015).

771

Freshwater ecosystem services supporting humans: Pivoting from water crisis to water

772

solutions. Global Environmental Change, 34, 108-118.

773

Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C., Bittner, L., ... & del Campo, J. (2012).

774

The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote

775

small sub-unit rRNA sequences with curated taxonomy. Nucleic acids research, 41(D1),

776

D597-D604.

777 778 779

Hanazato, T. (2001). Pesticide effects on freshwater zooplankton: an ecological perspective. Environmental pollution, 112(1), 1-10. Hanfling, B., Lawson Handley, L., Read, D. S., Hahn, C., Li, J., & Nichols, P., et al. (2016).

780

Environmental dna metabarcoding of lake fish communities reflects long-term data from

781

established survey methods. Molecular Ecology, 25(13), 3101-3119.

782 783 784

Hebert, P. D., & Gregory, T. R. (2005). The promise of DNA barcoding for taxonomy. Systematic biology, 54(5), 852-859. Hebert, P. D., Cywinska, A., Ball, S. L., & Dewaard, J. R. (2003). Biological identifications

785

through DNA barcodes. Proceedings of the Royal Society of London. Series B:

786

Biological Sciences, 270(1512), 313-321.

787

Hering, D., Borja, A., Jones, J. I., Pont, D., Boets, P., Bouchez, A., ... & Leese, F. (2018).

788

Implementation options for DNA-based identification into ecological status assessment

789

under the European Water Framework Directive. Water Research, 138, 192-205. 36

790 791 792

Hines, H. (2019). The biogeography, phylogeny, and dispersal of freshwater and terrestrial free-living ciliates in Florida, USA (Doctoral dissertation, Bournemouth University). Hirai, J., Kuriyama, M., Ichikawa, T., Hidaka, K., & Tsuda, A. (2015). A metagenetic

793

approach for revealing community structure of marine planktonic copepods. Molecular

794

Ecology Resources, 15(1), 68-80.

795 796

Humes, A. G. (1994). How many copepods?. In Ecology and morphology of copepods (pp. 1-7). Springer, Dordrecht.

797

Ide, K., Takahashi, K., Kuwata, A., Nakamachi, M., & Saito, H. (2008). A rapid analysis of

798

copepod feeding using FlowCAM. Journal of Plankton Research, 30(3), 275-281.

799

Ishii, K., & Fukui, M. (2001). Optimization of annealing temperature to reduce bias caused

800

by a primer mismatch in multitemplate PCR. Appl. Environ. Microbiol., 67(8),

801

3753-3755.

802

Jerde, C. L., Mahon, A. R., Chadderton, W. L., & Lodge, D. M. (2011). “Sight‐unseen”

803

detection of rare aquatic species using environmental DNA. Conservation Letters, 4(2),

804

150-157.

805

Jersabek, C. D., & Leitner, M. F. (2013): The Rotifer World Catalog. World Wide Web

806

electronic publication. http://www.rotifera.hausdernatur.at/, accessed December 12,

807

2019.

808

Ji, Y., Ashton, L., Pedley, S. M., Edwards, D. P., Tang, Y., Nakamura, A., … Yu, D. W. (2013).

809

Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecology

810

Letters, 16(10), 1245–1257.

811

Johnson, C. L., Runge, J. A., Curtis, K. A., Durbin, E. G., Hare, J. A., Incze, L. S., ... & Van 37

812

Guelpen, L. (2011). Biodiversity and ecosystem function in the Gulf of Maine: pattern

813

and role of zooplankton and pelagic nekton. PLoS One, 6(1), e16491.

814

Kelly, R. P., Port, J. A., Yamahara, K. M., Martone, R. G., Lowell, N., Thomsen, P. F., ... &

815

Crowder, L. B. (2014). Environmental monitoring. Harnessing DNA to improve

816

environmental management. Science (New York, NY), 344(6191), 1455.

817 818 819

Kreutz, M., Foissner, W. Protozoological Monographs, vol. 3. Shaker-Publishers, Aachen (2006) Kumar, S., Carlsen, T., Mevik, B. H., Enger, P., Blaalid, R., Shalchian-Tabrizi, K., &

820

Kauserud, H. (2011). C LOTU: An online pipeline for processing and clustering of 454

821

amplicon reads into OTUs followed by taxonomic annotation. BMC bioinformatics,

822

12(1), 182.

823

Kunin, V., Engelbrektson, A., Ochman, H., & Hugenholtz, P. (2010). Wrinkles in the rare

824

biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates.

825

Environmental Microbiology, 12(1), 118-123.

826 827 828

Layton, K. K., Martel, A. L., & Hebert, P. D. (2014). Patterns of DNA barcode variation in Canadian marine molluscs. PloS One, 9(4), e95003. Le Bourg, B., Cornet-Barthaux, V., Pagano, M., & Blanchot, J. (2015). FlowCAM as a tool

829

for studying small (80–1000 µm) metazooplankton communities. Journal of Plankton

830

Research, 37(4), 666-670.

831

Leese, F., Altermatt, F., Bouchez, A., Ekrem, T., Hering, D., Meissner, K., ... & Steinke, D.

832

(2016). DNAqua-Net: Developing new genetic tools for bioassessment and monitoring

833

of aquatic ecosystems in Europe. Research Ideas and Outcomes, 2, e11321. 38

834

Leray, M., Knowlton, N., Ho, S. L., Nguyen, B. N., & Machida, R. J. (2019). GenBank is a

835

reliable resource for 21st century biodiversity research. Proceedings of the National

836

Academy of Sciences, 116(45), 22651-22656.

837

Leray, M., Yang, J. Y., Meyer, C. P., Mills, S. C., Agudelo, N., Ranwez, V., ... & Machida, R.

838

J. (2013). A new versatile primer set targeting a short fragment of the mitochondrial COI

839

region for metabarcoding metazoan diversity: application for characterizing coral reef

840

fish gut contents. Frontiers in Zoology, 10(1), 34.

841

Li, F., Peng, Y., Fang, W., Altermatt, F., Xie, Y., Yang, J., & Zhang, X. (2018). Application of

842

environmental DNA metabarcoding for predicting anthropogenic pollution in rivers.

843

Environmental Science & Technology, 52(20), 11708-11719.

844

Lim, N. K. M. , Tay, Y. C. , Srivathsan, A. , Tan, J. W. T. , & Yeo, D. C. J. . (2016).

845

Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved

846

metazoan primer reveals species-rich and reservoir-specific communities. Royal Society

847

Open Science, 3(11).

848

Lim, Y. W., Kim, B. K., Kim, C., Jung, H. S., Kim, B. S., Lee, J. H., & Chun, J. (2010).

849

Assessment of soil fungal communities using pyrosequencing. The Journal of

850

Microbiology, 48(3), 284-289.

851

Lindeque, P. K., Parry, H. E., Harmer, R. A., Somerfield, P. J. and Atkinson, A. (2013) Next

852

generation sequencing reveals the hidden diversity of zooplankton assemblages. PLoS

853

One, 8, e81327.

854 855

Lynch, M. D., & Neufeld, J. D. (2015). Ecology and exploration of the rare biosphere. Nature Reviews Microbiology, 13(4), 217-229. 39

856

Mächler, E., Deiner, K., Steinmann, P., & Altermatt, F. (2014). Utility of environmental DNA

857

for monitoring rare and indicator macroinvertebrate species. Freshwater Science, 33(4),

858

1174-1183.

859

MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., &

860

Langtimm, C. A. (2002). Estimating site occupancy rates when detection probabilities

861

are less than one. Ecology, 83(8), 2248-2255.

862

Mahon, A. R., Barnes, M. A., Senapati, S., Feder, J. L., Darling, J. A., Chang, H. C., & Lodge,

863

D. M. (2011). Molecular detection of invasive species in heterogeneous mixtures using a

864

microfluidic carbon nanotube platform. PLoS One, 6(2), e17280.

865

Majaneva M, Diserud OH, Eagle SHC, Bostrom E, Hajibabaei M, Ekrem T. 2018.

866

Environmental DNA filtration techniques affect recovered biodiversity. Scitific Reports

867

8:4682.

868 869 870

Mashima, J., Kodama, Y., Fujisawa, T., Katayama, T., Okuda, Y., Kaminuma, E., ... & Takagi, T. (2016). DNA data bank of Japan. Nucleic acids research, gkw1001. McDonald, L. L. (2004). Sampling rare populations. In ‘Sampling rare or elusive species:

871

concepts, designs, and techniques for estimating population parameters’.(Ed. WL

872

Thompson) pp. 11–42.

873

Naito, A., Abe, Y., Matsuno, K., Nishizawa, B., Kanna, N., Sugiyama, S., & Yamaguchi, A.

874

(2019). Surface zooplankton size and taxonomic composition in Bowdoin Fjord,

875

north-western Greenland: A comparison of ZooScan, OPC and microscopic analyses.

876

Polar Science, 19, 120-129.

877

Nathan, L. M., Simmons, M., Wegleitner, B. J., Jerde, C. L., & Mahon, A. R. (2014). 40

878

Quantifying environmental DNA signals for aquatic invasive species across multiple

879

detection platforms. Environmental Science & Technology, 48(21), 12800-12806.

880

Nobles, T., & Zhang, Y. (2011). Biodiversity loss in freshwater mussels: importance, threats,

881

and solutions. Biodiversity loss in a changing planet, 318, 17-162.

882

Papa, R. D. S., Tordesillas, D. T., & Mamaril Sr, A. C. (2012). An updated taxonomic account

883

of limnetic crustacean zooplankton in Lake Taal, Philippines. Philippine Journal of

884

Science, 141(2), 243-252.

885

Pawlowski, J., Lejzerowicz, F., Apotheloz-Perret-Gentil, L., Visco, J., & Esling, P. (2016).

886

Protist metabarcoding and environmental biomonitoring: time for change. European

887

Journal of Protistology, 55, 12-25.

888 889 890 891 892

Payne, R. J. (2013). Seven reasons why protists make useful bioindicators. Acta Protozoologica, 52(3), 105. Pečnikar, Ž. F., & Buzan, E. V. (2014). 20 years since the introduction of DNA barcoding: from theory to application. Journal of Applied Genetics, 55(1), 43-52. Piaggio, A. J., Engeman, R. M., Hopken, M. W., Humphrey, J. S., Keacher, K. L., Bruce, W.

893

E., & Avery, M. L. (2014). Detecting an elusive invasive species: a diagnostic PCR to

894

detect B urmese python in F lorida waters and an assessment of persistence of

895

environmental DNA. Molecular Ecology Resources, 14(2), 374-380.

896

Poulton, N. J. (2016). FlowCam: quantification and classification of phytoplankton by

897

imaging flow cytometry. In Imaging Flow Cytometry (pp. 237-247). Humana Press,

898

New York, NY.

899

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., ... & Glöckner, F. O. 41

900

(2012). The SILVA ribosomal RNA gene database project: improved data processing and

901

web-based tools. Nucleic acids research, 41(D1), D590-D596.

902

Radulovici, A. E., SAINTE‐MARIE, B. E. R. N. A. R. D., & Dufresne, F. (2009). DNA

903

barcoding of marine crustaceans from the Estuary and Gulf of St Lawrence: a regional‐

904

scale approach. Molecular Ecology Resources, 9, 181-187.

905

Ratnasingham, S., & Hebert, P. D. (2007). BOLD: The Barcode of Life Data System

906

(http://www. barcodinglife. org). Molecular Ecology Notes, 7(3), 355-364.

907

Rees, H. C., Maddison, B. C., Middleditch, D. J., Patmore, J. R., & Gough, K. C. (2014). The

908

detection of aquatic animal species using environmental DNA–a review of eDNA as a

909

survey tool in ecology. Journal of Applied Ecology, 51(5), 1450-1459.

910

Sanoamuang, L. O. (1993). Comparative studies on scanning electron microscopy of trophi of

911

the genus Filinia Bory De St. Vincent (Rotifera). Hydrobiologia, 264(2), 115-128.

912

Scherer, C., Weber, A., Lambert, S., & Wagner, M. (2018). Interactions of microplastics with

913

freshwater biota. In Freshwater Microplastics (pp. 153-180). Springer, Cham.

914

Schmieder, R., & Edwards, R. (2011). Fast identification and removal of sequence

915 916

contamination from genomic and metagenomic datasets. PloS one, 6(3), e17288. Schultes, S., & Lopes, R. M. (2009). Laser Optical Plankton Counter and Zooscan

917

intercomparison in tropical and subtropical marine ecosystems. Limnology and

918

Oceanography: Methods, 7(11), 771-784.

919 920 921

Segers, H. H. (1995). A reappraisal of the Scaridiidae (Rotifera, Monogononta). Zoologica Scripta, 24(2), 91-100. Seymour, M., Durance, I., Cosby, B. J., Ransom-Jones, E., Deiner, K., Ormerod, S. J., ... & 42

922

Edwards, F. (2018). Acidity promotes degradation of multi-species environmental DNA

923

in lotic mesocosms. Communications biology, 1(1), 4.

924

Shaw, J. L. A., Clarke, L. J., Wedderburn, S. D. , Barnes, T. C., Weyrich, L. S., & Cooper, A.

925

(2016). Comparison of environmental dna metabarcoding and conventional fish survey

926

methods in a river system. Biological Conservation, 197, 131-138.

927

Shokralla, S., Spall, J. L., Gibson, J. F., & Hajibabaei, M. (2012). Next‐generation

928

sequencing technologies for environmental DNA research. Molecular Ecology, 21(8),

929

1794-1805.

930 931 932

Snell, T. W., & Marcial, H. S. (2017). Using Rotifers to Diagnosis the Ecological Impacts of Toxicants. In Rotifers (pp. 129-147). Springer, Singapore. Sogin, M. L., Morrison, H. G., Huber, J. A., Welch, D. M., Huse, S. M., Neal, P. R., ... &

933

Herndl, G. J. (2006). Microbial diversity in the deep sea and the underexplored “rare

934

biosphere”. Proceedings of the National Academy of Sciences, 103(32), 12115-12120.

935

Stanislawczyk, Keara Michelle, "Comparison of traditional microscopy and automated

936

imaging flow cytometry (FlowCAM) fordetecting and identifying rare zooplankton"

937

(2016). Electronic Theses and Dissertations. 5765.

938

Stat, M., Huggett, M. J., Bernasconi, R., DiBattista, J. D., Berry, T. E., Newman, S. J., ... &

939

Bunce, M. (2017). Ecosystem biomonitoring with eDNA: metabarcoding across the tree

940

of life in a tropical marine environment. Scientific Reports, 7(1), 12240.

941

Stuart, S. N., Chanson, J. S., Cox, N. A., Young, B. E., Rodrigues, A. S., Fischman, D. L., &

942

Waller, R. W. (2004). Status and trends of amphibian declines and extinctions worldwide.

943

Science, 306(5702), 1783-1786. 43

944

Sun, C., Zhao, Y., Li, H., Dong, Y., MacIsaac, H. J., & Zhan, A. (2015). Unreliable

945

quantitation of species abundance based on high-throughput sequencing data of

946

zooplankton communities. Aquatic Biology, 24(1), 9-15.

947 948 949

Taberlet, P., Bonin, A., Coissac, E., & Zinger, L. (2018). Environmental DNA: For biodiversity research and monitoring. Oxford University Press. Thomsen, P. F., Kielgast, J. O. S., Iversen, L. L., Wiuf, C., Rasmussen, M., Gilbert, M. T. P., ...

950

& Willerslev, E. (2012). Monitoring endangered freshwater biodiversity using

951

environmental DNA. Molecular Ecology, 21(11), 2565-2573.

952

Tréguier, A., Paillisson, J. M., Dejean, T., Valentini, A., Schlaepfer, M. A., & Roussel, J. M.

953

(2014). Environmental DNA surveillance for invertebrate species: advantages and

954

technical limitations to detect invasive crayfish P rocambarus clarkii in freshwater ponds.

955

Journal of Applied Ecology, 51(4), 871-879.

956

Turner, C. R., Uy, K. L., & Everhart, R. C. (2015). Fish environmental DNA is more

957

concentrated in aquatic sediments than surface water. Biological Conservation, 183,

958

93-102.

959

Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., … Dejean, T.

960

(2016). Next-generation monitoring of aquatic biodiversity using environmental dna

961

metabarcoding. Molecular Ecology, 25(4), 929-942.

962

Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., ...

963

& Davies, P. M. (2010). Global threats to human water security and river biodiversity.

964

Nature, 467(7315), 555.

965

Wang, J., McLenachan, P. A., Biggs, P. J., Winder, L. H., Schoenfeld, B. I., Narayan, V. V., ... 44

966

& Lockhart, P. J. (2013). Environmental bio-monitoring with high-throughput

967

sequencing. Briefings in Bioinformatics, 14(5), 575-588.

968

Wang, S., Xie, P., & Geng, H. (2010). The relative importance of physicochemical factors and

969

crustacean zooplankton as determinants of rotifer density and species distribution in

970

lakes adjacent to the Yangtze River, China. Limnologica, 40(1), 1-7.

971

Wang, Y., Castillo-Keller, M., Eustance, E., & Sommerfeld, M. (2017). Early detection and

972

quantification of zooplankton grazers in algal cultures by FlowCAM. Algal Research, 21,

973

98-102.

974

Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R., & Hebert, P. D. (2005). DNA barcoding

975

Australia's fish species. Philosophical Transactions of the Royal Society B: Biological

976

Sciences, 360(1462), 1847-1857.

977

Warren, D. M. (1996). Indigenous knowledge, biodiversity conservation and development.

978

Sustainable development in third world countries: Applied and Theoretical Perspectives,

979

81-88.

980

Westfall, K. M., Therriault, T. W., & Abbott, C. L. (2019). A new approach to molecular

981

biosurveillance of invasive species using DNA metabarcoding. Global Change Biology.

982

doi:10.1111/gcb.14886

983

Wilcox, T. M., McKelvey, K. S., Young, M. K., Jane, S. F., Lowe, W. H., Whiteley, A. R., &

984

Schwartz, M. K. (2013). Robust detection of rare species using environmental DNA: the

985

importance of primer specificity. PloS One, 8(3), e59520.

986 987

Williams, D. D. (2002). Temporary water crustaceans: biodiversity and habitat loss. In Modern approaches to the study of Crustacea (pp. 223-233). Springer, Boston, MA. 45

988 989 990

Wilson, H. B., Joseph, L. N., Moore, A. L., & Possingham, H. P. (2011). When should we save the most endangered species?. Ecology Letters, 14(9), 886-890. Wong, E., Sastri, A. R., Lin, F. S., & Hsieh, C. H. (2017). Modified FlowCAM procedure for

991

quantifying size distribution of zooplankton with sample recycling capacity. PloS One,

992

12(4), e0175235.

993

Xia, Z., Johansson, M. L., Gao, Y., Zhang, L., Haffner, G. D., MacIsaac, H. J., & Zhan, A.

994

(2018). Conventional versus real‐time quantitative PCR for rare species detection.

995

Ecology and Evolution, 8(23), 11799-11807.

996

Xiong, W., & Zhan, A. (2018). Testing clustering strategies for metabarcoding‐based

997

investigation of community–environment interactions. Molecular Ecology Resources,

998

18(6), 1326-1338.

999

Xiong, W., Li, H., & Zhan, A. (2016a). Early detection of invasive species in marine

1000

ecosystems using high-throughput sequencing: technical challenges and possible

1001

solutions. Marine Biology, 163(6), 139.

1002

Xiong, W., Li, J., Chen, Y., Shan, B., Wang, W., & Zhan, A. (2016b). Determinants of

1003

community structure of zooplankton in heavily polluted river ecosystems. Scientific

1004

Reports, 6, 22043.

1005

Xiong, W., Ni, P., Chen, Y., Gao, Y., Li, S., & Zhan, A. (2019). Biological consequences of

1006

environmental pollution in running water ecosystems: A case study in zooplankton.

1007

Environmental Pollution, 252, 1483-1490.

1008

Xiong, W., Ni, P., Chen, Y., Gao, Y., Shan, B., & Zhan, A. (2017). Zooplankton community

1009

structure along a pollution gradient at fine geographical scales in river ecosystems: the 46

1010 1011

importance of species sorting over dispersal. Molecular Ecology, 26, 4351-4360. Yang, J., Zhang, X., Xie, Y., Song, C., & Burton, G. A. (2017a). Zooplankton community

1012

profiling in a eutrophic freshwater ecosystem-lake Tai basin by DNA metabarcoding.

1013

Scientific Reports, 7(1773), 1-11.

1014

Yang, J., Zhang, X., Xie, Y., Song, C., Sun, J., Zhang, Y., ... & Yu, H. (2017b). Ecogenomics

1015

of zooplankton community reveals ecological threshold of ammonia nitrogen.

1016

Environmental Science & Technology, 51(5), 3057-3064.

1017

Yang, Y., Li, S., Gao, Y., Chen, Y., & Zhan, A. (2019). Environment-driven geographical

1018

distribution of bacterial communities and identification of indicator taxa in Songhua

1019

River. Ecological Indicators, 101, 62-70.

1020

Zhan, A., & MacIsaac, H. J. (2015). Rare biosphere exploration using high-throughput

1021

sequencing: research progress and perspectives. Conservation Genetics, 16(3), 513-522.

1022

Zhan, A., Bailey, S. A., Heath, D. D., & Macisaac, H. J. (2014a). Performance comparison of

1023

genetic markers for high-throughput sequencing-based biodiversity assessment in

1024

complex communities. Molecular Ecology Resources, 14(5), 1049-1059.

1025

Zhan, A., He, S., Brown, E. A., Chain, F. J., Therriault, T. W., Abbott, C. L., ... & MacIsaac,

1026

H. J. (2014b). Reproducibility of pyrosequencing data for biodiversity assessment in

1027

complex communities. Methods in Ecology and Evolution, 5(9), 881-890.

1028

Zhan, A., Hulák, M., Sylvester, F., Huang, X., Adebayo, A. A., Abbott, C. L., ... & MacIsaac,

1029

H. J. (2013). High sensitivity of 454 pyrosequencing for detection of rare species in

1030

aquatic communities. Methods in Ecology and Evolution, 4(6), 558-565.

1031

Zhan, A., Xiong, W., He, S., & MacIsaac, H. J. (2014). Influence of artifact removal on rare 47

1032

species recovery in natural complex communities using high-throughput sequencing.

1033

PloS One, 9(5), e96928.

1034

Zhang, G. K., Chain, F. J., Abbott, C. L., & Cristescu, M. E. (2018). Metabarcoding using

1035

multiplexed markers increases species detection in complex zooplankton communities.

1036

Evolutionary Applications, 11(10), 1901-1914.

1037

Zhou, X., Li, Y., Liu, S., Yang, Q., Su, X., Zhou, L., ... & Huang, Q. (2013). Ultra-deep

1038

sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples

1039

without PCR amplification. Gigascience, 2(1), 4.

48

1040

Table 1 The most recent and important reference databases for zooplankton Database

Organisms

name

covered

EMBL-E

Bacteria/Archaea,

16S/23S rRNA

BI

Eukaryota

16S/18S/ COI

GenBank

Bacteria/Archaea,

16S/23S rRNA

Eukaryota

16S/18S/ COI

Bacteria/Archaea,

16S/23S rRNA

Eukaryota

16S/18S/ COI

Bacteria/Archaea,

16S/23S

Eukaryota

18S/28S

Animals,

COI

Plants,

rbcl, matk

Fungi

ITS

Protists

18S

DDBJ

SILVA

BOLD

PR2

Genetic markers

Establishe

Website

Reference

1980

https://www.ebi.ac.uk/ena

--

1982

https://www.ncbi.nlm.nih.g

Leray et al.

ov/genbank/

(2019)

https://www.ddbj.nig.ac.jp/i

Mashima

ndex-e.html

al. (2016)

https://www.arb-silva.de/

Quast et al.

d year

1987

2007

et

(2012) 2007

http://www.boldsystems.org

Ratnasingha

/

m & Hebert (2007)

2012

ssu-rrna.org/pr2

Guillou et al. (2013)

1041

49

1042

Figure captions:

1043

Figure 1 Photos of zooplankton observed by traditional morphological methods referred

1044

from Rotifer World Catalog (Jersabek & Leitner 2013). A: Brachionus angularis Gosse, 1851;

1045

B: Brachionus diversicornis (Daday, 1883); C: Keratella cochlearis (Gosse, 1851); D:

1046

Brachionus forficula Wierzejaki, 1891; E: Brachionus quadridentatus Hermann, 1783; F:

1047

Keratella cochlearis (Gosse, 1851); G: Trichotria tetractis (Ehrenberg, 1830).

1048

Figure 2 Morphology-based methods for zooplankton biodiversity monitoring.

1049

Figure 3 Workflow and key technical points of metabarcoding in biodiversity monitoring of

1050

zooplankton.

1051

Figure 4 Technical difficulties and possible solutions in flowchart of biodiversity monitoring

1052

of zooplankton.

50

1053

Figure 1

1054 1055

51

1056

Figure 2

1057 1058

52

1059

Figure 3

1060 1061

53

1062

Figure 4

1063

54

Highlights 1.

Freshwater ecosystems and associated biodiversity have been highly degraded.

2.

Biodiversity monitoring is crucial for diagnosing degradation degrees

3.

Here we review available methods for monitoring zooplankton biodiversity.

4.

We propose possible solutions for existing technical issues.