Genome

Considerations for incorporating real-time PCR assays into routine marine biosecurity surveillance programmes: a case study targeting the Mediterranean fanworm (Sabella spallanzanii) and club tunicate (Styela clava)

Journal: Genome

Manuscript ID gen-2018-0021.R3

Manuscript Type: Article

Date Submitted by the 19-Jun-2018 Author:

Complete List of Authors: WOOD, Susanna; Cawthron Institute, Pochon, Xavier;Draft Cawthron Institute; University of Auckland Ming, Witold; Cawthron Institute von Ammon, Ulla; Cawthron Institute; University of Auckland Woods, Chris; National Institute of Water & Atmospheric Research Ltd Carter, Megan; National Institute of Water & Atmospheric Research Ltd Smith, Matt; National Institute of Water & Atmospheric Research Ltd Inglis , Graeme ; National Institute of Water & Atmospheric Research Ltd Zaiko, Anastasija ; Cawthron Institute; University of Auckland

Environmental DNA and RNA, Non-indigenous species, Occupancy Keyword: models, Real-time Polymerase Chain Reaction, Surveillance

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1 Considerations for incorporating real-time PCR assays into routine marine biosecurity

2 surveillance programmes: a case study targeting the Mediterranean fanworm (Sabella

3 spallanzanii) and club tunicate (Styela clava)

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6 Susanna A Wood1*, Xavier Pochon1,3, Witold Ming1, Ulla von Ammon1,2, Chris Woods4, Megan

7 Carter4, Matt Smith4, Graeme Inglis4, Anastasija Zaiko1,2

8 1 Coastal and Freshwater Group, Cawthron Institute, Nelson, New Zealand 9 2 School of Biological Sciences, University of Auckland, Auckland, New Zealand 10 3 Institute of Marine Science, University of Auckland, Auckland, New Zealand 11 4 National Institute of Water & Atmospheric Research Ltd, New Zealand 12 13 *corresponding authors: Coastal and Freshwater Group, Cawthron Institute, 98 Halifax Street East, 14 7010, Nelson, New Zealand. [email protected] 15 16

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32 Abstract

33 Molecular techniques may provide effective tools to enhance marine biosecurity surveillance.

34 Prior to routine implementation, evidence-based consideration of their benefits and limitations is

35 needed. In this study, we assessed the efficiency and practicality of visual diver surveys and real-time PCR

36 assays (targeting DNA and RNA) for detecting two marine invasive species whose infestation levels varied

37 between species and location; Sabella spallanzanii and Styela clava. Filtered water samples (n=171)

38 were collected in parallel with dive surveys atDraft two locations as part of the New Zealand Marine High Risk Site

39 Surveillance programme: Nelson Harbour (27 sites) and Waitemata Harbour (30 sites). Diver surveys

40 resulted in a greater number of detections compared to real-time PCR: S. clava – 21 versus 5 sites

41 in Nelson, 6 versus 1 in Auckland; S. spallanzanii – 18 versus 10 in Auckland, no detections in

42 Nelson. Occupancy modelling derived detection probabilities for the real-time PCR for S. clava

43 were low (14%), compared to S. spallanzanii (66%). This could be related to abundances, or

44 species-specific differences in DNA shedding. Only one RNA sample was positive, suggesting

45 that most detections were from extracellular DNA or non-viable fragments. While molecular

46 methods cannot yet replace visual observations, this study shows they provide useful

47 complementary information.

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49 Key words: Environmental DNA and RNA, Non-indigenous species, Occupancy models, Real-

50 time Polymerase Chain Reaction, Surveillance

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52 Introduction

53 The arrival and establishment of marine non-indigenous species can have dramatic effects on the

54 structure and functioning of coastal ecosystems (Galil 2007; Wallentinus and Nyberg 2007;

55 Ehrenfeld 2010). Early detection and monitoring of marine non-indigenous species has become a

56 priority in many countries. Surveillance programmes commonly rely on techniques that use

57 morphological identification of organisms detected in samples or in situ, e.g., visual surveys by

58 divers (Cohen et al. 2001; Hewitt and Martin 2001; Inglis et al. 2006). Divers can detect moderate-

59 sized organisms even when present at relatively low densities, with limited water visibility (> 0.8

60 m Secchi depth) (Inglis et al. 2006). However, smaller individuals, especially when they are

61 amongst complex biofouling assemblages, may be overlooked. Utilization of divers to search for

62 marine non-indigenous species may alsoDraft not be possible at certain times/locations due to health

63 and safety concerns.

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65 Molecular approaches are being advocated to circumvent some of the limitations of

66 morphological-based detection and monitoring methods in aquatic systems (e.g., Comtet et al.

67 2015). A suite of different techniques has been developed and applied in the last decade, with the

68 most commonly employed now being barcoding, real-time (or quantitative) polymerase chain

69 reaction (PCR) and metabarcoding using high-throughput sequencing (Ficetola et al. 2008;

70 Thomsen et al. 2012a; Wood et al. 2013). In aquatic systems, these techniques rely on collecting

71 and detecting either: (i) entire organisms (particularly in the case of micro-organism such as

72 bacteria, micro-algae, zooplankton), (ii) cells of organisms which may originate from various

73 sources, including scales, faeces, epidermal mucus, urine, saliva and gametes (Barnes et al. 2014),

74 or (iii) extracellular DNA which can be free-floating or particle-bound. These techniques have

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75 been applied to determine aquatic biodiversity or the presence (and in some cases abundance) of

76 specific taxa in a wide range of habitats (e.g., Thomsen et al. 2012b; Dowles et al. 2016; Laroche

77 et al. 2016; Ulibarri et al. 2017; Keeley et al. 2018). Studies have shown that in some situations

78 molecular methods can be more efficient for detecting species than traditional approaches (Dejean

79 et al. 2012; Keskin 2014; Zaiko et al. 2016), making them a promising tool for the early detection

80 of newly introduced species and for monitoring the dispersal of established taxa (Blanchet 2012;

81 Piaggio et al. 2014; Rees et al. 2014; Comtet et al. 2015).

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83 To date, most studies in aquatic environments investigating molecular techniques, DNA stability,

84 and appropriate sampling methods have targeted fish (Thomsen et al. 2012a; Takahara et al. 2013),

85 crustaceans (Forsströma and VasemägiDraft 2016), amphibians (Dejean et al. 2012), and

86 macroinvertebrates (Mächler et al. 2014). While sensitive and specific molecular assays have been

87 developed to detect sessile marine non-indigenous species (Gillium et al. 2014; Simpson et al.

88 2017; Wood et al. 2017), experimental and field studies to assess applicability of using these in

89 routine marine biosecurity programmes are lacking. Few studies have directly compared molecular

90 techniques with traditional visual survey methods (Ulibarri et al. 2017). Before molecular methods

91 can be routinely applied for surveillance, more information is required on achievable detection

92 rates and optimal sampling methods. Better understanding of what the assays are detecting (i.e.,

93 free-floating DNA, non-viable fragments of organisms, or living organisms (propagules)) is also

94 needed for providing robust advice to effectively guide management decisions.

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96 In the present study, we focus on two marine non-indigenous species that are already established

97 in New Zealand coastal waters; Sabella spallanzanii and Styela clava. The Mediterranean fanworm

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98 S. spallanzanii (Gmelin, 1791) (Polychaeta: Sabellidae) is a large, tube-dwelling polychaete worm

99 native to the Mediterranean Sea and Atlantic coast of Europe (Patti and Gambi 2001). Once

100 established, it can form dense populations (100’s to 1000’s per m2) covering a variety of marine

101 habitats (e.g., Holloway and Keough 2002). It was first detected in New Zealand in 2008 (Read et

102 al. 2011) and has been detected at multiple locations, including several at which it is now well

103 established (for specific distribution data please refer to https://www.marinebiosecurity.org.nz).

104 The club tunicate S. clava, Herdman, 1881 (Ascidiacea: Styelidae) is thought to be native to the

105 northwest Pacific (Japan, Korea, Northern China, and Siberia). It too can form dense populations

106 (100’s to 1000’s per m2) covering a variety of marine habitats once established (e.g., Clarke and

107 Therriault 2007; Davis and Davis 2010). It was first detected in New Zealand in 2005 (Davis and

108 Davis 2006), and is nowDraft relatively widespread and established

109 (https://www.marinebiosecurity.org.nz). Both these non-indigenous species present significant

110 ecological, economic and societal values risk to New Zealand (e.g., Soliman and Inglis 2018), and

111 are part of a suite of non-indigenous species subject to targeted surveillance in 11 major New

112 Zealand ports/marinas within the national Marine High Risk Site Surveillance (MHRSS)

113 programme (Inglis et al. 2006; Woods et al. 2017).

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115 To compare the efficiency of species detection by visual diver surveys and real-time PCR assays, we targeted

116 two locations (Nelson and Auckland) with varying prevalence of the target species. In Nelson,

117 despite occasional detections of S. spallanzanii (usually on transient vessels or marina pontoons),

118 no established populations are known. In contrast, S. spallanzanii is well established in Auckland

119 (Woods et al. 2017), with populations reaching extremely high densities at some sites (e.g., 100-

120 600 individuals per m2, NIWA unpubl. data). Styela clava is present throughout Nelson Harbour,

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121 particularly in the port and marina areas (Woods et al. 2017), but only at low densities (typically

122 ≤ 2 individuals per m2, but up to 50 per m2, NIWA unpubl. data). In Auckland S. clava populations

123 are present throughout the harbour, particularly in port and marina areas, but only at low densities

124 (typically ≤ 5 individuals per m2, but up to 50 per m2, NIWA unpubl. data).

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126 Real-time PCR assays can provide semi-quantitative data; however, for larger organisms this is

127 rarely used because gene copy numbers may differ between organisms, cells and life stages. In this

128 study, we used real-time PCR assays with standard curves to generate data on gene copy numbers

129 to enable comparison with the abundance of individuals observed via visual diver surveys. We

130 also incorporated RNA analysis into this study to infer the source of any positive signals.

131 Environmental RNA is thought to degradeDraft within minutes to hours, and therefore is expected to

132 provide a better proxy for characterizing living organisms (Pochon et al. 2017). Thus, a signal

133 from only the DNA samples may indicate the detection was due to extracellular DNA or fragments

134 of the target organisms, whereas detection in both the DNA and RNA samples would suggest the

135 presence of living individuals, most likely larvae.

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137 The objectives of the present study were: (i) to evaluate the applicability and sensitivity of two

138 surveillance approaches by comparing detection success in two locations with varying levels of

139 infestation; (ii) to determine the source of any molecular signal (living organisms, or non-viable

140 fragments or extracellular DNA), and (iii) to define considerations required when incorporating

141 real-time PCR assays into marine biosecurity surveillance programmes.

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143 Methods

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144 Sites and sampling

145 Sampling was undertaken at ports, marinas and surrounding coastline at two locations in New

146 Zealand, Nelson and Auckland (Fig. 1). Triplicate water samples were collected from a boat from

147 27 sites in Nelson on 12 and 13 February 2017, and 30 sites in Auckland on 19 and 20 April 2017.

148 Water sampling sites were within 3–5 m of the point of entry of the dive surveys (see below). The

149 water depth at each site was measured, and 15 L of seawater was pumped (5.0 GPM Washdown

150 Pump, Seaflow, China) through a pre-filter (20-µm netting) from three depths; 1 m above the

151 seafloor, 1 m below the water surface, and mid-way between these two depths (total volume = 45

152 L). Seawater (ca. 50 mL) from the site was added to the filtrate captured on the mesh and this was

153 filtered a second time (sterile Whatman GF/C, c. 1.6 µm pore size, 47 mm). The filter was halved

154 using sterile scissors, and half filters wereDraft placed into two separate cryotubes (2 mL), immediately

155 frozen in liquid nitrogen and then stored frozen (-80°C) for later DNA and RNA extraction (in

156 Nelson), or placed in sterile Eppendorf tubes containing LifeGuard Preservation Solution (1 mL;

157 QIAGEN, CA, U SA) and within 36 hours stored at -80°C for later DNA and RNA extraction (in

158 Auckland). Logistical constraints prevented us travelling with liquid nitrogen. Preliminary

159 experiments with samples collected from Nelson marina and stored in both LifeGuard Preservation

160 Solution and liquid nitrogen showed similar amplification of S. clava (data not shown). Prior to

161 sampling at each site, all sampling equipment was thoroughly washed using 2% bleach (sodium

162 hypochlorite) solution for at least 5 mins, and rinsed in seawater from the sampling site.

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164 At each site, two occupationally-certified scientific divers using self-contained underwater

165 breathing apparatus (SCUBA) visually assessed up to 10 wooden pilings each or the equivalent

166 area of a continuous substratum (typically ca. 50 m of floating pontoon, steel wall, rock/concrete

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167 breakwall or riprap) to a maximum water depth of 5 m (see Inglis et al. 2006). These dive surveys

168 were conducted as part of the bi-annual MHRSS surveying of each location (Woods et al. 2017).

169 When jetty/wharf piles were surveyed by the divers, the total number of piles with one or more

170 individuals of S. spallanzanii or S. clava observed as epibionts, not the number of individuals

171 observed per pile, are given. For pontoons or other continuous substratum, the average data from

172 the two divers (in individuals per m2) are given. These surveys were undertaken on 12 and 13

173 February 2017 (Nelson) and on 23 and 24 May 2017 (Auckland).

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175 DNA and RNA extractions and cDNA synthesis

176 Each step of the molecular analysis (i.e., DNA extraction, real-time PCR setup, template addition

177 and real-time PCR analysis) was conductedDraft in a separate sterile laboratory dedicated to that step

178 with sequential work flow to ensure no cross-contamination. Each room was equipped with ultra-

179 violet sterilization which was switched on for a minimum of 15 min before and after each use. The

180 PCR set-up and template addition was undertaken in laminar flow cabinets with HEPA filtration.

181 Aerosol barrier tips (Axygen BioScience, CA, USA) were used throughout.

182

183 The filters from Nelson were placed into ZR BashingBead Lysis Tubes (2.0 mm; Zymo Research,

184 CA, USA) containing Lysis Buffer (1 mL) from the ZR-Duet™ DNA/RNA MiniPrep Kit (Zymo

185 Research). For the Auckland samples, the tubes containing LifeGuard were centrifuged (3,000 ×

186 g, 2 min) and the supernatant removed. The filter was transferred to the BashingBead Lysis Tube

187 using sterile forceps and any remaining pellet was mixed with Lysis Buffer and then transferred to

188 the same tube. The tubes were then placed on a bead beater (1600MiniG Spex SamplePrep, NJ,

189 USA) for 2 min at 1500 RPM. DNA and RNA were co-extracted from filters using the ZR-Duet™

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190 DNA/RNA MiniPrep Kit (Zymo Research), according to the manufacturer’s protocol. The quality

191 and purity of isolated DNA and RNA were checked using a BioPhotometer (Eppendorf, Leipzig,

192 Germany; FileS1).

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194 Extracted RNA of all corresponding DNA samples that tested positive using the S. spallanzanii or

195 S. clava real-time assays (see below) were then processed by eliminating trace DNA molecules

196 carried over in RNA extracts using two DNase treatments as in Langlet et al. (2013). The efficiency

197 of the DNase treatment was verified by running a real-time PCR using the reagents and conditions

198 described below for the S. spallanzanii or S. clava assay. The RNA samples were then reverse

199 transcribed into cDNA using the SuperScript III reverse transcriptase (Life Technologies, CA,

200 USA). To confirm that cDNA was produced,Draft the universal primers Uni18SF and Uni18SR (Zhan

201 et al. 2013) were used to amplify the eukaryotic V4 region of the nuclear small subunit ribosomal

202 DNA (18S rRNA) gene. These PCR amplifications were undertaken on an Eppendorf Mastercycler

203 (Eppendorf, Germany) in a total volume of 25 μL using 12.5 µL AmpliTaq Gold® 360 PCR Master

204 Mix (Life Technologies), 2.5 μL GC enhancer, 1 µL of each primer (10 µM, Integrated DNA

205 Technologies [IDT], CA, USA), 5 µL RNA/DNA free water (UltraPure, Life Technologies), 2 µL

206 Bovine Serum Albumin (BSA; 0.2 mg/mL, Sigma, USA) and 1 µL of template cDNA. Reaction

207 cycling conditions were: 95°C for 10 min, followed by 35 cycles of 95°C for 30 s, 50°C for 30 s,

208 72°C for 60 s, and a final extension of 72°C for 7 min. PCR products were visualized on a 1.5%

209 agarose gel, stained with RedSafe™ Nucleic Acid Staining Solution (iNtRON Biotechnology,

210 Gyeonggi-do, Korea).

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212 Real-time PCR analysis of samples

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213 All samples were screened in duplicate for inhibition using an internal inhibition control assay.

214 Each 10 µL reaction contained 6.25 µL KAPA Probe Fast QPCR Kit Master Mix (2×), 0.5 µL of

215 primers targeting the Internal Transcribed Spacer region 2 of the rRNA gene operon of

216 Oncorhynchus keta salmon sperm (10 µM, Sketa F2 and Sketa R3, IDT, USA, Haugland et al.

217 2005), 0.2 µL TaqMan probe (10 µM) labelled at the 5’ end with the fluorescent reporter dye

218 FAM-6-carboxyfluorecein and at the 3’ end with a non-fluorescent quencher with the Black Hole

219 Quencher-1 (IDT, USA), 0.8 µL DNA/RNA free water (Life Technologies, USA), 0.75 µL BSA

220 (Sigma), 1 µL extracted salmon sperm DNA (15 ng; Sigma, USA) and 1 µL of template DNA.

221 The cycling profile was: 95°C for 3 min, followed by 40 cycles at 95°C for 3 s and 60°C for 20 s.

222 All samples showed some inhibition and were diluted 1:10 with DNA/RNA free water (Life

223 Technologies, USA) and reanalysed forDraft inhibition as described above.

224

225 Each sample was then analysed in triplicate for S. spallanzanii and S. clava using the real-time

226 assays described in Wood et al. (2017) and Gillium et al. (2014), both targeting the mitochondrial

227 cytochrome c oxidase I (COI) gene. Each specific reaction consisted of 10 µL containing: 6.25 µL

228 KAPA Probe Fast QPCR Kit Master Mix (2×), 0.4 µL of the primers Sab3-QPCR-F and Sab3-

229 QPCR-R for S. spallanzanii (10 µM, IDT, Wood et al. 2017) and SC1F and SC1R for S. clava (10

230 µM, IDT, Gillum et al. 2014), 0.3 µL TaqMan probe synthesised with a FAM reporter dye at the

231 5´-end and a Black Hole Quencher-2 at the 3´-end, i.e. Sab3-QPCR-Probe for S. spallanzanii (10

232 µM, IDT, Wood et al. 2017) and SC1 for S. clava (10 µM, IDT, Gillum et al. 2014), 0.75 µL BSA

233 (Sigma), 0.8 µL DNA/RNA free water (Life Technologies, USA) and 1 µL of template DNA. The

234 cycling profile used for both assays was: 95°C for 3 min, followed by 40 cycles at 95°C for 3 s

235 and 60°C for 20 s. Five point standard curves ranging from 7.56 x 102 to 106 gene copies per µL

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236 (S. spallanzanii) and 1.66 x 103 to 107 gene copies per µL for S. clava and no template controls

237 were analyzed in triplicate on each real-time PCR run. The standard curve was constructed using

238 a purified (AxyPrep PCR Clean-up Kit, Axygen Biosciences, USA) PCR product generated using

239 the S. spallanzanii and S. clava primers described above. The number of copies in the PCR product

240 used for the standard curves were determined using: (A × 6.022 × 1023) / (B × 1×109 × 650), with

241 A being the concentration of the PCR product, 6.022 × 1023 (Avogadro's number), B being the

242 length of the PCR product, 1×109 used to convert to ng, and 650 the average molecular weight per

243 base pair. The standard curves were linear (R2>0.98) and real-time PCR efficiency ranged between

244 0.80 to 0.96. When positive samples were obtained using the DNA samples, the corresponding

245 cDNA sample was analysed in triplicate using the corresponding real-time PCR assays described

246 above. Draft

247

248 Data analysis

249 Samples were considered positive when the target gene was present in at least two of the three

250 triplicates analysed. To compare detection probabilities for the two target genes and two detection

251 methods (visual surveys by divers and real-time PCR assays), a multi-method occupancy model

252 (Nichols et al. 2008) was used based on the mark-recapture-like approach (Mac-Kenzie et al. 2002)

253 implemented in PRESENCE v12.7 (Hines 2006). In the model, each sampling site (i.e., wharf,

254 breakwall, pontoon pier etc.; Figs. 1 and 2) represented a sample unit. For visual observations,

255 each diver’s survey at a site was treated as a sampling occasion. For the real-time PCR assay

256 method, each of the triplicate water samples was considered as an independent sampling occasion.

257

258 The following parameters were defined in the models for each target gene:

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m 259 pt = probability of detection at occasion t by method m, given that sample unit is occupied and

260 the species is present at the immediate sampling site;

261 ψ = probability of sample unit being occupied by a species;

262 θt = probability of a species being present at a sampling site at occasion t, given that sample unit

263 is occupied (expected to approach 1 for sessile species);

264 휓 = naïve estimate of occupancy probability, calculated as proportion of sample units where the

265 species was detected over all units surveyed.

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267 A small set of a priori single season multi-method models to describe the detection data were

268 defined. Since the analysis was primarily aimed at comparing species detection efficiency by two

269 methods, the pre-defined models do notDraft necessarily represent all environmental factors that

270 influenced the probability of occupancy of the two species. For S. clava, location was considered

271 as a single unit-specific covariate and its effect on large-scale probability of occupancy tested: ψ(H)

272 and ψ, respectively for location-dependent and location-independent occupancy. For S.

273 spallanzanii, no covariates were applied (no detection reported for Nelson). For both species, the

274 probability of detection was modelled as either constant or different between detection methods:

275 p(m) and p, respectively for method-dependent and method- independent detection. All fitted

276 models were ranked according to Akaike Information Criterion (AIC) values calculated in

277 PRESENCE.

278

279 Results

280 Styela clava

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281 Styela clava was observed by divers at 21 of the 27 surveyed Nelson sites (Table 1, Fig. 1). The

282 highest densities reported were 2 individuals per m2 and when piles were surveyed, at two sites

283 one or more individuals were observed on 5 of the 10 piles surveyed (Table 1). In contrast, S. clava

284 was only detected in 5 of the 27 Nelson sites by real-time PCR. At four of these sites, it was only

285 detected in one of the triplicate samples collected at that location (Table 1). Calculated gene copy

286 numbers ranged from 3,840 to 14,400 per sample. At sites with sufficient replicates, where

287 standard errors could be calculated, variability in gene copy number per site was high (23-67%;

288 Table 1). There were three sites (sites 4, 14 and 16) where both diver surveys and the real-time

289 PCR assays detected S. clava, and two sites (sites 20, 22) where it was detected only by real-time

290 PCR (Table 1 and Fig. 1). These two sites were in the inner regions of the marina (Fig. 1). The

291 limited number of sites (n=3) where bothDraft methods detected S. clava prevented any statistical

292 analysis of the relationship between visual density and copy number to be undertaken; however,

293 there was some indication that higher gene copy numbers were associated with denser settlements

294 (e.g., site 4 and 14). The cDNA of all DNA samples that were positive returned no real-time PCR

295 signal.

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297 In Auckland, S. clava was identified by divers at 6 of the 19 sites surveyed with a maximum density

298 of 1 per m2, and when piles were surveyed, at one site, one or more individuals were observed on

299 5 of the 10 piles surveyed (Table 1). The real-time PCR assay was only positive for one of these

300 19 sites (site 39; Table 1). There were an additional three sites where S. clava was detected by the

301 real-time PCR assay, but diver surveys were not undertaken due to operational/safety reasons

302 (Table 1, Fig. 1). Variability in gene copy number within a site was high (Table 1), and gene copy

303 numbers per sample were on average ca. 50-fold higher than those measured in Nelson (Table 1).

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304 There was only one site (site 39) where both the visual survey and real-time PCR assay (only one

305 of the three replicates) detected S. clava, and this was the site with the highest density of S. clava

306 observed by divers (Table 1). The cDNA from one of the samples from site 29 was positive for S.

307 clava using the real-time PCR assay. All three replicates were positive with an average gene copy

308 number per sample of 71,200 ± 11,520 (standard error).

309

310 Sabella spallanzanii

311 No S. spallanzanii was observed during the dive survey or detected using the real-time PCR at any

312 of the Nelson sites. In Auckland, S. spallanzanii was identified by divers at 18 of the 19 sites

313 surveyed, with maximum densities of 30 per m2. When piles were surveyed, at one site, one or

314 more individuals were observed on 10 Draftof the 10 piles surveyed (Table 1, Fig. 2). The real-time

315 PCR assay detected S. spallanzanii at 20 of the 30 total sites surveyed in Auckland, including 10

316 of the 19 sites also surveyed by divers. In most instances S. spallanzanii was detected in 2 or 3

317 replicate samples taken at each site, with gene copy numbers ranging from 18,750 ± 7,400 to

318 1,205,740 ± 150,013 per sample (Table 1).

319

320 The limited number of samples with positive detection, and difficulties with using the visual data

321 in two different formats (i.e. per m2 and per no. of piles), prevented any robust statistical correlation

322 to be undertaken between visual survey density and real-time PCR copy numbers. However,

323 inspection of the data suggests no or very weak relationships. No S. spallanzanii was detected by

324 real-time PCR assay at three sites with the highest densities determined visually (sites 37, 38 and

325 42; Table 1), and conversely high copy numbers were detected at sites where visual surveys

326 reported only moderate densities (i.e., sites 35, 45; Table 1). There were no obvious spatial patterns

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327 to explain the lack of detection via real-time PCR where divers observed this species. For example,

328 there were positive and negative detections using the real-time assay within the same marina

329 (distance between sites ca. 1,000 m), when all visual surveys detected the species (sites 53, 54).

330 No positive S. spallanzanii signals were detected in the cDNA samples.

331

332 Occupancy and detection probabilities

333 Naïve estimates of large-scale occupancy (휓 ) were considerably higher for visual surveys

334 compared to real-time PCR assay results for both species and both locations (Fig. 3). However, for

335 S. clava, occupancy estimates were consistently (ca. two-fold) higher for Nelson compared to

336 Auckland. Modelled estimates of small-scale occupancy (θ), as expected, were always 1, since

337 targeted sessile benthic species cannot vacateDraft the sampling site between sampling occasions. Both

338 large-scale occupancy (ψ) and detection probability (p) estimates yielded good precision in the

339 top-ranked models, as evidenced by the standard error values (never exceeded 30% of the estimate

340 value, Table 2). Consistent with naïve estimates, there was evidence that ψ for S. clava differed

341 between Auckland and Nelson. For both species, models accounting for varying detection

342 probabilities between methods had considerably higher Akaike weights compared to those not

343 including method as a factor, suggesting very strong evidence for method-specific detection. In all

344 cases probability of detection was higher for visual surveys. This was overall noticeably higher for

345 S. spallanzanii compared to S. clava, reaching 100% detection using visual surveys.

346

347 Discussion

348 The present study investigated the effectiveness of collecting and filtering water samples and using

349 real-time PCR assays to detect two non-indigenous marine benthic sessile species: the

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350 Mediterranean fan worm S. spallanzanii, and the club tunicate S. clava. Our results concur with the

351 study of Mächler et al. (2014) who observed that for some species (they studied a gastropod, an

352 amphipod and two Trichoptera), traditional detection methods are more efficient than eDNA

353 methods. Likewise, Ulibarri et al. (2017) compared the American Fisheries Society Standard

354 Snorkelling technique with DNA sampling (15 mL water samples taken every 10 m over a 100-m

355 reach) combined with real-time PCR analysis, and also showed greater detection rates with the

356 traditional method. Here, we discuss the implications of our findings in the context of factors that

357 should be considered when including eDNA approaches into routine marine biosecurity

358 surveillance programmes.

359

360 In the present study, there were numerousDraft sites where divers observed the target species but these

361 were not detected by the real-time PCR assays. Considering detection rates reported from other

362 similar studies e.g., 57% for the mud crab (Forsström and Vasemägi 2016), 82% for dragonflies,

363 100% for toads (Thomsen et al. 2012b) and 59% for crayfish (Tréguier et al. 2014), the detection

364 probabilities derived for real-time PCR from the best-fitted occupancy model were relatively low

365 for S. clava (14%), but comparable for S. spallanzanii (66%). Direct comparisons with other

366 studies are challenging due to differences in target species morphology, density and life stage,

367 habitat, and sampling detection and analysis methods (Kelly et al 2014; Tréguier et al. 2014;

368 Deiner et al. 2015). The lower detection probabilities yielded by real-time PCR assays compared

369 to visual diver surveys in the current study suggest that molecular-based analyses should not be

370 used as the only detection method for the two considered species for marine surveillance purposes.

371 It is important to understand the reasons for lower detection using a molecular-based technique,

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372 and to consider the potential for improvement with further development of sampling

373 methodologies and molecular-based technologies.

374

375 Reasons for the low detection rate using the real-time PCR assays and differences between the two

376 species are likely the result of a combination of factors. Although in this study the limited sample

377 numbers prevented any correlative statistical analysis between the methodologies, the data suggest

378 there would only be a weak relationship between copy number and numbers of individuals at a

379 site. However, at a population scale there may be relationships. For example, for higher density of

380 S. spallanzanii in Auckland, the detection probabilities were much higher compared to similar

381 occupancy levels of S. clava in Nelson. Other researchers have shown that the amount of target

382 DNA detected in a water body is influencedDraft by species density (Takahara et al. 2012; Pilliod et al.

383 2013).

384

385 There is limited knowledge on how the amount of cell/extracellular DNA released from different

386 organisms varies. Biological form and life-style may affect this process; for example, S.

387 spallanzanii has a two-layered crown of hundreds of fine tentacles which extend out of the tube

388 during feeding. It seems more likely that pieces may break off these and be distributed in ambient

389 environment, compared to the more rigid and streamlined form of S. clava. However, S. clava

390 expels water through its siphon which may include DNA. In a study of three marine fish Sassoubre

391 et al. (2016) demonstrated variable eDNA shedding and decay rates, and attributed this to different

392 sources i.e., scales versus mucus, as well as fish size and possibly physiologies, metabolic rates

393 and feeding activities. DNA degradation rates are also likely to be species-specific (Sassoubre et

394 al. 2016) and affected by environmental factors such as water temperature, pH and sunlight

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395 (Dejean et al. 2011; Strickler et al. 2015; Andruszkiewicz et al. 2017). More information on the

396 source and longevity of DNA in the marine environment is urgently needed to assist in optimizing

397 sampling efforts and aid in the interpretation of results in routine marine biosecurity surveillance

398 programmes.

399

400 Research is required to determine the source of DNA from S. spallanzanii and S. clava. This may

401 also influence the most optimal sample collection methodology. In this study, we used a 20 µm

402 pre-filter followed by filtration on to a GF/C filter (1.6 µm pore size) to enable relatively large

403 volumes of water to be sampled. Using these methods any ‘free’ extracellular DNA would have

404 passed directly through the pre-filter, and therefore we were likely capturing extracellular DNA

405 attached to particles, or non-viable fragmentsDraft of the organisms. Previous studies have shown that

406 there is no ideal sampling method to capture all environmental DNA in a sample (Deiner et al.

407 2015; Sassoubre et al. 2016). Additionally, larger sampling volumes may not result in higher

408 detection rates; for example, Mächler et al. (2016) showed that the volume of water sampled had

409 no effect on detection for two of three studied species, whereas detection improved for the third

410 species as the volume of water filtered increased. Site topography and location may have also had

411 an effect on the real-time PCR detections. For example, sites 24-27 in Nelson are exposed to the

412 ocean with water column being constantly mixed by wind and tidal currents. Although much of

413 the water in the Nelson marina would be exchanged through tidal fluxes, either side of the tide

414 stand there would be periods where the water would be relatively stagnant, perhaps increasing

415 chances of detection. Timing molecular sampling close to the slack water period of the tidal cycle

416 may enhance detection.

417

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418 At each site, there was high variability in detection via real-time PCR and a number of instances

419 where only one or two of the samples were positive. Copy numbers also varied markedly among

420 biological replicates. This, in contrast to other studies which have shown high consistency in

421 biological replicates (Forsströma and Vasemägi 2016), further highlights the likelihood that there

422 is species-specific variability in the quantities and distributions of DNA in marine systems. To

423 date, most environmental DNA studies in marine environments have focused on motile species,

424 e.g. fish and crustaceans (Forsström and Vasemägi 2016; Sassoubre et al. 2016), and little attention

425 has been given to sessile species which are often associated with marine bioinvasions. The high

426 variability in copy number among biological replicates also reinforces the need to carefully

427 consider adequate sampling effort, taking into account specific biological traits of target species

428 and environmental characteristics of theDraft sampling locations.

429

430 In Nelson, there were two sites where S. clava was detected by real-time PCR but divers did not

431 observe this species. These sites were in the inner regions of the marina with relatively limited

432 visibility (ca. 1–2 m), with potentially lower likelihood of species detection by divers. It is also

433 likely that there were specimens at the nearby locations (i.e., site 21, where S. clava was observed

434 by divers, was less than 100 m away) and that the samples contained DNA or fragments of

435 organisms from this close-by site. As noted above, the presence of DNA signal may have been

436 greater at these sites in the inner region of the marina due to reduced water exchange with the

437 coastal waters. DNA is not constrained by any barriers in aquatic systems; thus a limitation of

438 DNA sampling is that it does not necessarily provide any quantitative information, nor does it

439 provide accurate information on the exact location of the target taxa. Therefore, in a marine

440 biosecurity surveillance programme, a positive signal from a molecular technique would need to

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441 be followed up with a more specific detection technique (e.g., visual method), or target sampling

442 of other matrices, e.g., biofouling, with molecular detection to confirm the presence of the target

443 taxa.

444

445 The lack of positive signals in all but one (site 29, S. clava) of the cDNA samples suggests that

446 most of the real-time PCR detections were from extracellular DNA (either free DNA, DNA bound

447 to organisms/inorganic particles), or non-viable fragments of organisms, rather than living

448 organisms. Environmental RNA is known to degrade within minutes to hours (Stoeck et al. 2007;

449 Orsi et al. 2013), and therefore is expected to provide a better proxy for the detection of living

450 organisms than DNA (Pochon et al. 2017). This detection possibly corresponds to the capture of

451 larvae as our sampling took place duringDraft the known spawning season for S. clava in New Zealand

452 (late summer to early autumn, Wong et al. 2011), although we might have expected more positive

453 detections given this scenario. Specialized collection, storage and workflow protocols (e.g.,

454 preservation buffers, dedicated instruments and sample preparation rooms), are required when

455 working with RNA, and the reverse transcription step adds considerable expense and time to the

456 sample processing. For routine marine surveillance programme a more affordable approach should

457 involve focusing primarily on DNA with additional samples (for both molecular and

458 morphological analysis) collected following positive detections.

459

460 Most marine organisms have planktonic gamete/larval stages, and although these will be widely

461 dispersed by physical processes, appointing sampling to this propagule dispersal period would

462 almost certainly enhance chances of collecting and detecting target organisms (especially those

463 not constantly present in the water column). However, planktonic life stages of many marine

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464 invasive species are relatively limited (2–3 weeks), and it would be hard to co-ordinate large

465 national scale surveillance programmes to fit these time frames, especially given constraints such

466 as the need for relatively calm and stable weather for sampling. For many invasive species, the

467 reproductive season (in a new environment) is unknown and unlikely to match that of their native

468 locations due to environmental differences. In countries such as New Zealand, that span a

469 latitudinal gradient of twelve degrees (between -35° and -47° south) and where the temperature of

470 coastal waters varies from more than 24°C in the north to less than 7°C in the south, reproductive

471 periods can vary markedly. Timing national marine biosecurity surveillance programmes to align

472 with reproductive season for multiple species would be very challenging. Additionally, many

473 species arrive attached to vessels/structures as mature adults, and many do not reach reproductive

474 stages for many months; therefore, relyingDraft on sampling methods that capture only larval stages is

475 unlikely to be successful and advantageous from a biosecurity management perspective. As

476 suggested by Hayes et al. (2005), it is advisable to use multiple methods targeting different life-

477 stages and different environmental matrices where traces of species presence can be detected.

478

479 The results of the visual dive surveys are instantaneous, albeit in some cases specimens might need

480 confirmative identification by a skilled taxonomist or via confirmatory molecular testing, i.e. real-

481 time PCR or barcoding. In contrast, with the number of molecular samples collected per location

482 (81–90) and using the real-time PCR methods employed in this study, full molecular results took

483 2–3 weeks to generate post sampling. The addition of the dual DNA/RNA extraction, and when

484 required reverse transcription steps and testing of cDNA samples, added additional time that would

485 not be required in a routine surveillance programme. Even for the present study, where a relatively

486 limited number of sites were sampled, this constituted >2,300 real-time PCR reactions; at an

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487 estimated 15 NZ dollars per sample (not including capital, machine running and labour expenses).

488 Digital droplet PCR (ddPCR) offers potential for rapidly expediting this process as no inhibition

489 assays are required, no standard curves are needed, and samples do not need to be run in triplicate

490 as essentially every result is an average of ca. 20,000 individual PCR reactions (Doi et al. 2015;

491 Te et al, 2015).

492

493 Technology is advancing rapidly and there is an increasing push for the development of portable

494 and cheap real-time PCR machines and assays that allow for on-site analysis (e.g., Tsai et al. 2012;

495 Chua et al. 2016). More regular sampling with the ability of managers to analyse samples rapidly

496 on site offers huge potential to enhance marine biosecurity surveillance in the future. One

497 limitation of real-time PCR assays is Draft that they only target one or a few species, and rely on

498 predictions of which species are most likely to arrive and become problematic at a given location.

499 While methods such as metabarcoding can overcome these limitations and allow characterisation

500 of entire biological communities (Taberlet et al. 2012; Díaz-Ferguson et al. 2014; Comtet et al.

501 2015; Pochon et al. 2017), the steps involved in sample preparation are relatively protracted and it

502 is unlikely they will be employed for on-site rapid detection in the near future.

503

504 The occupancy-modelling approach employed in the present study proved a useful method to

505 account for imperfect detection of the two target species. It provided a valuable analytical

506 framework for estimating the different sensitivities of the visual and real-time PCR methods for

507 the two study species, and we recommend its application to similar comparative studies.

508

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509 This study has highlighted that there are uncertainties related to the use of molecular methods to

510 detect marine non-indigenous species, and limitations that may need further development to

511 overcome. Critical questions that need to be addressed include:

512 - Which sampling methods are most effective and is there one method that can capture the

513 DNA of multiple target species?

514 - Should sampling be undertaken at specific times of the day or year?

515 - How long does environmental DNA/RNA remain intact in marine environments and does

516 this vary between species?

517 Considerable research is required to answer these uncertainties, and it’s clear that molecular

518 methods cannot yet be used as a substitute for observations made by experienced field personnel

519 (Ulibarri et al. 2017). However, these Draft methods could be used to complement existing marine

520 surveillance techniques. This would enhance marine biosecurity surveillance through their ability

521 for sensitive and specific detection, and because they are able to identify cryptic species or life

522 stages (Smith et al. 2012). The incorporation of these methods into routine sampling programmes

523 would also expedite the development of more rapid and robust sampling techniques, and aid in the

524 development of novel molecular-focused sampling equipment.

525

526 Acknowledgements

527 We thank Janet Adamson (Cawthron) for technical assistance in the laboratory and Marc Jary

528 (Cawthron) for assistance preparing for field sampling. We thank the New Zealand Ministry for

529 Primary Industries for access to data generated as part of the Marine High Risk Site Surveillance

530 programme, and Abraham Growcott as MPI’s Operational Liaison. This work was supported by

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531 the National Institute of Water and Atmospheric Research Ltd under Coasts and Oceans Research

532 Programme 6, Marine Biosecurity (SCI 2016-18).

533

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Table 1. Diver survey and real-time PCR results from DNA samples. No Sabella spallanzanii was detected via either method in Nelson. p = number of piles surveyed (data are a total from two dive surveyors and shows the number of piles with S. clava, not the number of individuals). Data in m2 are average values from two divers who each surveyed ca. 50 m of continuous substratum (e.g., pontoon). S = number of samples (from a total of 3) taken per site that the target was detected in. Real-time PCR data are an average of n=2 or 3 replicates per sample. Samples where less than two of the triplicates were positive were excluded. The volume of water sampled was 45 L.

Nelson Auckland Real-time PCR Real-time PCR Real-time PCR Site Visual Site Visual S. Visual S. S. clava S. clava (copies/sample ± S. spallanzanii no. S. clava no. clava spallanzanii (copies/sample ± 1SE) 1SE) (copies/sample ±1 SE) 1 1.5 /m2 0 28 NS 117,000 (1S) NS 451,010 (±124,430; 3S) 2 1 /m2 0 29 NS 213,600 (1S) NS 603,840 (± 94,770; 2S) 3 2 (10 p) 0 30 NS 0 NS 1,094,690 (± 230,850; 3S) 4 3 (10 p) 14,400 (1S) 31 NS 1,372,800 (± 897,600; 2S) NS 715,260 (±189,120; 3S) 5 1 (10 p) 0 32 0 0 20 /m2 31,320 (± 21,060; 2S) 6 0.5 /m2 0 33 NS 0 NS 278,060 (± 39,500; 3S) 7 2 /m2 0 34 NS 0 NS 282,600 (± 118,700; 2S) 8 5 (10 p) 0 35 1 (10 p) 0 4.5 (10 p) 1,205,740 (± 150,013; 3S) 9 1 (10 p) 0 36 NS 0 NS 39,210 (± 12,870; 2S) Draft 2 10 5 (10 p) 0 37 0 0 25 /m 0 11 2 (5 p and pontoon) 0 38 0 0 30 /m2 0 12 1 /m2 0 39 5 (10 p) 352,000 (±143,980; 1S) 12 (20 p) 374,350 (± 85,170; 3S) 13 0 0 40 0 0 20 /m2 35,360 (± 13,490; 2S) 14 3 /m2 14,400 (± 9,700; 1S) 41 0 0 20 /m2 144,920 (± 75,580; 2S) 15 1 (1 p and pontoon) 0 42 NS 0 NS 233,050 (± 102,420; 3S) 16 1 (1 p and pontoon) 5,400 (1S) 43 NS 0 NS 100,020 (± 26,630; 2S) 17 4 (10 p) 0 44 3 (10 p) 0 10 (20 p) 72,710 (± 12,970; 3S) 18 2 (10 p) 0 45 0 0 6 (20 p) 770,280 (± 78,789; 3S) 19 0 0 46 0 0 30 /m2 0 20 0 3,840 (± 960; 2S) 47 NS 0 NS 512,222 (± 69,680; 2S) 21 0.5 /m2 0 48 0 0 10 (10 p) 0 22 0 9,200 (± 2,120; 1S) 49 0 0 10 (10 p) 0 23 0 0 50 2 (5 p) 0 10 (10 p) 18,750 (± 7,400; 1S) 24 0 0 51 1 /m2 0 3 /m2 136,560 (± 62,590; 1S) 25 0.5 /m2 0 52 0 0 5 /m2 113,480 (± 46,060; 3S) 26 0.5 /m2 0 53 0.5 /m2 0 10 /m2 0 27 1.25 /m2 0 54 0 0 17.5 /m2 0 55 0 0 0 0 56 NS 0 NS 0 57 0 0 2 /m2 0

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Table 2. Summary of model selection statistics for Steyla clava and Sabella spallanzanii detection data. Akaike Information Criteria (AIC), calculated conservatively using the number of sampling sites as the effective sample size (57 and 30 for S. clava and S. spallanzanii, respectively). AICw represents Akaike weight of the model, K the number of parameters in the model and -2Log(L) is twice the negative log- likelihood value. Detection probabilities may vary among methods (m) and large-scale probability of occupancy, for S. clava may vary between locations. For the top-ranked models the following parameter estimates are shown: large-scale probability of occupancy (ψ), small-scale probability of occupancy (θ), detection probability (p). Where applicable, standard errors (SE) are given.

Styela clava Sabella spallanzanii

Model AIC AICw K -2Log(L) Model AIC AICw K -2Log(L) ψ(H),θ,p(m) 190.9 0.95 5 180.9 ψ,θ,p(m) 90.92 1 4 82.92 ψ,θ,p(m) 197.2 0.04 4 189.2 ψ,θ,p 110.62 0 3 104.62 ψ(H),θ,p 213.8 0 4 205.8 ψ,θ,p 222.29 0 3 216.3

Averaged estimates from the top-ranked models Location Real-time PCR Visual survey Real-time PCR Visual survey Auckland ψ(SE) 0.45 (0.12) 0.45 (0.12)Draft 0.96 (0.04) 0.96 (0.04) θ(SE) 1 1 1 1 p(SE) 0.14 (0.04) 0.54 (0.09) 0.66 (0.06) 1

Nelson ψ(SE) 0.92 (0.11) 0.92 (0.11) θ(SE) 1 1 p(SE) 0.14 (0.04) 0.54 (0.09)

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Figure captions

Fig. 1. Location of sampling sites in Nelson (a) and Auckland (b), and results of diver surveys and

real-time PCR assay for the detection of Styela clava. See Table 1 for density and gene copy

number information. Diver surveys were not undertaken at all Auckland sampling sites due to

operational/safety restrictions.

Fig. 2. Location of sampling sites in Auckland (New Zealand), and results of diver surveys and

real-time PCR assay for the detection of Sabella spallanzanii. See Table 1 for density and copy

number information. No S. spallanzannii was detected at any of the Nelson sites via real-time or diver survey. Diver surveys were not undertaken at all Auckland sites due to operational/safety restrictions. Draft

Fig. 3. Occupancy probability for (a) Steyla clava, and (b) Sabella spallanzanii in Nelson (dark grey) and Auckland (light grey): naïve estimates (휓 , mean + 1 standard error (SE) from real-time

PCR and visual observations data and model estimates (ψ, mean + 1 SE) from the best selected

model (see Table 2).

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