Stable isotopes reveal the importance of saltmarsh-derived nutrition for two exploited penaeid prawn species in a seagrass dominated system

Journal Pre-proof Stable isotopes reveal the importance of saltmarsh-derived nutrition for two exploited penaeid prawn species in a seagrass dominated system Daniel E. Hewitt, Timothy M. Smith, Vincent Raoult, Matthew D. Taylor, Troy F. Gaston PII:

S0272-7714(19)30508-6

DOI:

https://doi.org/10.1016/j.ecss.2020.106622

Reference:

YECSS 106622

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 25 May 2019 Revised Date:

6 January 2020

Accepted Date: 23 January 2020

Please cite this article as: Hewitt, D.E., Smith, T.M., Raoult, V., Taylor, M.D., Gaston, T.F., Stable isotopes reveal the importance of saltmarsh-derived nutrition for two exploited penaeid prawn species in a seagrass dominated system, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/ j.ecss.2020.106622. 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. © 2020 Published by Elsevier Ltd.

1

Stable isotopes reveal the importance of saltmarsh-derived nutrition for two exploited

2

penaeid prawn species in a seagrass dominated system

3 4

Daniel E. Hewitt1,*, Timothy M. Smith1, Vincent Raoult1, Matthew D. Taylor1, 2, Troy F.

5

Gaston1

6

1

7

2258, Australia

8

2

9

Locked Bag 1, Nelson Bay, NSW, 2315, Australia

10

School of Environmental and Life Sciences, University of Newcastle, Ourimbah, NSW,

Port Stephens Fisheries Institute, New South Wales Department of Primary Industries,

* Corresponding author: [email protected]

11 12

Running Title: Saltmarsh-derived nutrition for prawns

13

Abstract

14

Estuaries represent highly important nursery habitats for a range of species, with refuge and

15

nutrition being two key benefits derived from estuaries. Quantifying these benefits provides

16

us with a means for enhancing fisheries productivity. Metapenaeus macleayi (School Prawn)

17

and Penaeus plebejus (Eastern King Prawn) are two commercially and recreationally

18

important species in New South Wales that utilise estuarine nurseries throughout their life

19

history. In this study, stable isotopes of carbon, nitrogen and sulfur were used to determine

20

the proportional contribution of primary producers to prawn nutrition in Brisbane Water

21

(NSW). Both the saltmarsh grass Sporobolus virginicus and seagrass Zostera muelleri were

22

found to support a high trophic contribution to prawns (up to 53 % and 40 %, respectively).

23

The contributions of other primary producers such as mangroves, fine benthic organic matter

24

(FBOM) and C3 saltmarsh plants were generally found to be much lower (0.7 – 15 %). Such

25

findings are generally consistent with patterns observed in other south-east Australian

26

estuaries, however such a dominant role of saltmarsh in the presence of seagrass is a novel

27

finding. These results highlight linkages between habitats of conservation concern and highly

28

valuable fisheries species, and the benefit of using sulfur as an additional marker in Bayesian

29

mixing models examining mixing in estuary food webs.

30 31

Keywords: Saltmarsh restoration; Shrimp; Sulfur; Bayesian mixing model; Fisheries

32

productivity

33 34

2

35

1. Introduction

36

Estuaries represent some of the most productive systems in the world, supporting a range of

37

ecosystem services (see Costanza et al., 1997 for discussion). In particular, estuaries fulfil a

38

nursery function for many exploited species through their early life history stages (Beck et

39

al., 2001). Estuaries generally include a mosaic of sub-, inter- and supratidal vegetated

40

habitats such as seagrass, mangroves and saltmarsh. These habitats provide foraging

41

resources (Melville and Connolly, 2003, Melville and Connolly, 2005) and shelter or refuge

42

(Ochwada et al., 2009) for juveniles, and generally support growth and survival through

43

vulnerable early life history stages (Haas et al., 2004). As a result the productivity of

44

commercially important species, and the fisheries they support, is inherently linked to the

45

availability and adequate functioning of these habitats.

46 47

Globally, estuarine habitats, such as saltmarshes, support high abundances of important

48

fisheries species, including several species of decapod crustaceans such as crabs (i.e.

49

Calinectes sapidus; Dittel et al., 2000) and penaeid prawns (i.e. Farfantapenaeus aztecus;

50

Fry, 2008, Minello et al., 2003). Research from the United States suggests that a combination

51

of the outwelling of detrital material (Odum, 2000) and translocation of nutrients via

52

consumption and subsequent movement of consumers (Kneib, 2000) form pathways for the

53

transfer of energy that ultimately support fisheries production (Hyndes et al., 2014). Despite

54

the widely acknowledged importance of estuarine habitats for certain life stages of fisheries

55

species, information regarding the exact roles of specific habitats is still lacking in even the

56

most extensively studied regions (e.g. the Gulf of Mexico; Fry, 2008, Abrantes et al., 2015a)

57

precluding a throrough understanding of the basis of fisheries production.

58 59

In an Australian context, penaeid prawns support a significant proportion of the value of

60

wild-harvest fisheries (Mobsby and Koduah, 2017). Metapenaeus macleayi (School Prawn)

61

and Penaeus plebejus (Eastern King Prawn) are two commercially exploited species endemic

62

to the east coast of Australia. Both exhibit a Type-II biphasic life cycle, which includes an

63

estuarine (juvenile) phase and an oceanic (adult) phase (Dall et al., 1990), implying a reliance

64

on the aforementioned estuarine habitats. Similar to systems in the U.S. it has been suggested

65

that indirect linkages, such as the outwelling of trophic productivity may be the primary way

66

that saltmarshes benefit these species (Taylor et al., 2017a), given they exhibit minimal direct

67

interaction with the marsh surface (Becker and Taylor, 2017).

68 3

69

The importance of saltmarsh-derived material to both School Prawn and Eastern King Prawn

70

has been demonstrated for a number of temperate south-east Australian estuaries such as the

71

Clarence and Hunter River (Taylor et al., 2017a, Raoult et al., 2018), however both of these

72

study systems are seagrass-limited. Similarly seagrass has also been found to be an important

73

source of nutrition for penaeid prawns both in the presence (Loneragan et al., 1997) and

74

absence of saltmarsh systems (e.g. Saco and Sangala Bays, Mozambique; de Abreu et al.,

75

2017), and as such it is difficult to generalise these patterns to other systems where these

76

habitats coincide. Other primary producers such as phytoplankton and epiphytic algae are

77

also an important source of nutrition for penaeid species (Primavera, 1996). In contrast,

78

mangroves appear to provide lower contributions (Taylor et al., 2017a, Raoult et al., 2018,

79

Chong et al., 2001). Inspite of some historical uncertainty regarding the exact roles of these

80

habitats, broad-scale relationships between their areal extent and the productivity of penaeid

81

fisheries have been clearly demonstrated (Turner, 1977, Saintilan and Wen, 2012, Loneragan

82

et al., 2013).

83 84

Despite the importance of estuaries, these systems are subject to significant and increasing

85

anthropogenic pressures (Rogers et al., 2015). Direct impacts such as clearing, draining and

86

reclamation of intertidal habitats to make way for development are compounded by the

87

indirect effects of alterations to tidal regimes, increased urban run-off and sea-level rise,

88

which has resulted in significant and widespread habitat loss (Kennish, 2002, Worm et al.,

89

2006, Waycott et al., 2009). These losses are estimated to be 29, 50 and 35 % globally for

90

seagrass, saltmarsh and mangrove, respectively (Barbier et al., 2011). In Australia as much as

91

28 % of coastal aquatic systems are modified to some extent (National Land and Water

92

Resources Audit, 2002) equating to losses of up to 62000 ha (~72 %) of ‘prime fish habitat’

93

(which includes mangrove and saltmarsh) in New South Wales alone since European

94

settlement (Rogers et al., 2015). Such extensive loss and degradation of estuarine habitats

95

inhibits estuarine nursery function, which in turn constrains fisheries productivity (Creighton

96

et al., 2015, Rogers et al., 2015). Recently, a business case has been developed suggesting

97

habitat repair and restoration targeted to benefit high-value fisheries species, such as School

98

Prawn and Eastern King Prawn (Creighton et al., 2015, Taylor, 2016), however, such efforts

99

are dependent upon establishing quantifiable links between these habitats and the fisheries

100

they support.

101

4

102

Stable isotopes are a powerful tool for elucidating linkages between primary producers and

103

the organisms they support (Fry, 2006). The isotopic composition of a consumers tissue

104

provides a time-integrated estimation of assimilated diet (Hobson, 1999). Stable isotopes can

105

be used to trace the source of nutrition (13C; Bouillon et al., 2011), and assign trophic levels

106

to consumers within a system (15N; Zanden and Rasmussen, 2001). Stable isotope analysis is

107

most effective in systems where sources of nutrition are well separated in their isotopic

108

composition (Phillips et al., 2014), therefore, similarity in the carbon isotopic composition of

109

some primary producers, such as saltmarsh and seagrass, has made it difficult to determine

110

the relative contributions of these sources (Melville and Connolly, 2005). The incorporation

111

of additional stable isotope tracers, such as sulfur, which is particularly useful in anaerobic

112

systems (Fry et al., 1982), has recently been employed as a method to circumvent this issue

113

(Hindell and Warry, 2010, Wilson et al., 2010, Currin et al., 2011, Duffill Telsnig et al.,

114

2019). This is due to the broad range of inorganic sources of sulfur available to marine and

115

estuarine primary producers (Peterson et al., 1985, Fry et al., 1982). However, until recently,

116

stable isotope mixing models were limited in terms of the number of tracers they could

117

incorporate (Phillips and Gregg, 2003). The advent of Bayesian mixing models, which can

118

incorporate a greater number of tracers, has overcome this limitation providing a means for

119

assessing the relative contributions of estuarine habitats that may be similar in their isotopic

120

composition (Parnell et al., 2013). The inclusion of another tracer also has the added benefit

121

of being able to include a greater number of potential sources without inducing inaccuracies

122

in Bayesian mixing models (Parnell et al., 2010).

123 124

This study sought to determine the dominant basal sources of nutrition for School Prawn and

125

Eastern King Prawns (collectively “prawns”) in the Brisbane Water estuary, New South

126

Wales. Specifically, we intended to resolve the ambiguity regarding contributions of seagrass

127

and saltmarsh in the presence of one another. This was achieved by characterising the

128

isotopic composition of primary producers available within the estuary and applying a

129

Bayesian mixing model to estimate their contribution to the diet of prawns within the estuary.

130 131

2. Methods

132

2.1 Study system

133

This study was conducted in Brisbane Water, a wave-dominated barrier estuary (Roy et al.,

134

2001), situated on the temperate south-east Australian coast of New South Wales,

135

approximately 50 km north of Sydney. It is characterised by a single, permanently open, 5

136

narrow entrance (~ 150 m wide) with a main tidal channel that branches into several basins at

137

distances of 6 – 8 km inland (Ford et al., 2006). It is fed by several small tributaries including

138

Narara Creek and Erina Creek in the north, and Kincumber to the south-east. The catchment

139

includes urban, industrial and semi-rural land use (Cardno Lawson Treolar, 2008). The

140

foreshore of the estuary has been extensively modified to make way for urban development,

141

and losses of ~78 % of saltmarsh have been recorded (Harty and Cheng, 2003). As is typical

142

in south-east Australian estuaries, mangrove forests line much of the shore (~ 207 ha)

143

between the open water and saltmarsh (~ 112 ha), and extensive seagrass beds also

144

characterize many of the basins throughout the estuary (~ 561 ha; Fig. 1).

145 146

2.2 Sample collection

147

Prawns (n ≥ 3) and their potential food sources were randomly sampled in triplicate from four

148

sites (1 – 4) at varying distances from the mouth of the estuary (Fig. 1; Supplementary

149

Material Table 1). Sites were chosen where the range of possible primary producers (i.e.

150

mangroves, seagrass, saltmarsh grass and succulents) are known to be present. The almost

151

cosmopolitan distribution of seagrass beds in nearshore habitats across the estuary meant all

152

prawns were collected from within these habitats. Primary producers sampled included

153

Avicennia marina (mangrove), mangrove pneumatophore epiphytes (MPE), fine benthic

154

organic matter (FBOM; includes detritus, microphytobenthos, sediment and other biological

155

material), Sporobolus virginicus (Salt Couch), Sarcocornia quinqueflora (Beaded Samphire),

156

Suaeda australis (Austral Seablite), Juncus kraussi (Sea Rush), Zostera muelleri (Eelgrass),

157

seagrass epiphytes and particulate organic matter (POM). Sites were chosen where seagrass,

158

saltmarsh and mangroves were present and to provide spatially diverse sampling across the

159

systems. Other species of seagrass (i.e. Posidonia australis, Halophila ovalis) represent a

160

small fraction of the total seagrass extent within the estuary and were not included in this

161

study. All plants and epiphytes were collected by hand. FBOM was collected by scraping

162

~100 mL of surface sediment, while 1 L samples of seawater were collected for subsequent

163

POM extraction. Prawns were collected from seagrass beds via beach seine (10 m x 1.2 m

164

drop with a 10 mm stretch mesh). We attempted to sample 10 individuals of each species at

165

each site, however, in all instances this was not achievable, and beach seines were set until at

166

least 3 prawns of each species were collected. Increasing the number of individuals collected

167

and analysed will increase the accuracy of model outputs (Pearson and Grove, 2013).

168

Samples were immediately placed on ice and frozen at -20°C until subsequent processing.

169 6

170

2.3 Laboratory analysis

171

Frozen samples were thawed prior to preparation for isotope analysis. Muscle tissue was

172

extracted from the tail of prawns by removing the head, gut and any remaining shell

173

fragments (Mazumder et al., 2008). FBOM was sieved from bulk sediment samples (Saintilan

174

and Mazumder, 2010). FBOM and epiphytic (i.e. seagrass and mangrove) samples were not

175

acid (HCl) treated (following Mazumder et al., 2010), as the fraction of sediment in these

176

samples was low – any sediment or calcareous material was manually excluded from these

177

samples. However, it should be noted that other calcareous material (containing inorganic

178

carbon) may have persisted which can alter δ13C values (Yokoyama et al., 2005, Schlacher

179

and Connolly, 2014). POM samples were obtained by filtering seawater samples onto a pre-

180

combusted glass fibre filter paper under low vacuum, pre-filtration of POM samples was not

181

required as the samples had low turbidity and did not contain any large organic material.

182

Zostera epiphytes were combined to make composite samples at each site, as there was

183

insufficient material for individual samples. All other plant and prawn tissue samples were

184

prepared and analysed individually by being rinsed with deionised water and placed in

185

individual HCl-rinsed glass petri dishes, dried at 60°C for 24 h and then ground to a fine

186

powder using a Retsch Mixer Mill MM200. Ground samples were then placed into plastic

187

vials and sent to Griffith University, Queensland, for stable isotope analysis using a Secron

188

Hydra 20-22 automated Isoprime Isotope Ratio Mass Spectrometer. The standards used to

189

compare isotope contents were: Vienna Pee Dee Belemnite for carbon, air for nitrogen and

190

Vienna Canon Diablo meteorite troilite for sulfur. Stable isotope composition was expressed

191

in delta-notation using conventional formulae (Fry, 2006).

192 193

2.4 Data analysis

194

All following statistical analysis were undertaken using R v. 3.4.4 (R Development Core

195

Team, 2013). Bayesian mixing models were used to determine the proportional contribution

196

of each primary producer (referred to as “sources” in a Bayesian framework) to School Prawn

197

and Eastern King Prawn (referred to as “consumers” in a Bayesian framework), using

198

MixSIAR (Stock and Semmens, 2016, available at https://github.com/brianstock/MixSIAR/,

199

Parnell et al., 2013; available at https://github.com/andrewcparnell/simmr). Since the

200

likelihood of producing accurate predictions of source contributions in a Bayesian model is

201

inversely related to the number of sources in the model (Parnell et al., 2010), analysis of

202

variance (ANOVA) was used to determine whether it was possible to pool sources that did

203

not have significantly different isotopic signatures, thereby decreasing the number of sources 7

204

in our model and increasing the likelihood of producing accurate predictions of source

205

contribution.

206 207

Bayesian mixing models are contingent upon two primary assumptions: 1) that all dietary

208

sources are included in analyses and, 2) that there is complete mixing (Phillips et al., 2014).

209

To ensure that the requirements of the former assumption were met, we attempted to sample

210

all known dominant primary producers in the system (Roy et al., 2001), and the species we

211

sampled were the known dominant saltmarsh, mangrove and seagrass species (Saintilan et al.,

212

2013). While it is difficult to assess whether all potential sources were included in such an

213

open system, we included all the primary producers typically included in research in this area

214

(Melville and Connolly, 2003, Connolly and Waltham, 2015, Taylor et al., 2017a, Raoult et

215

al., 2018). Furthermore, we assessed the suitability of applying our mixing models using the

216

point-in-polygon simulation as developed by Smith et al. (2013; script available for download

217

at http://www.famer.unsw.edu.au/downloads.html), which assesses the “completeness” of the

218

isotopic data obtained by simulating the variability in the mixing polygon, as defined by the

219

TEFs of each source, over a user-defined number of iterations and calculating the probability

220

that the consumers within the model lie within the polygon. Muscle tissue was used as a long-

221

term indicator of diet preference (Hewitt et al., 2018), reducing the possible temporal

222

variability in source availability. Since we only measured muscle isotope composition our

223

model cannot explicitly determine the proportions of sources consumed, only the proportion

224

assimilated into tissues.

225 226

Within MixSIAR, the trophic enrichment factor (TEF) was set to 1, 1.95 and 0.5 ‰ for δ13C,

227

δ15N and δ34S, respectively (as determined for a broad range of crustaceans; Vanderklift and

228

Ponsard, 2003, McCutchan et al., 2003, Abrantes et al., 2015b). The standard deviation for

229

the TEF for δ15N was set to 1.65 ‰ (Vanderklift and Ponsard, 2003), for δ13C and δ34S the

230

TEF standard deviation was set to 1.5 ‰ as a conservative measure to reflect uncertainties

231

regarding the TEFs (McCutchan et al., 2003, Raoult et al., 2018), as trophic levels and

232

enrichment factors are known to strongly affect Bayesian models (Caut et al., 2009, Bond and

233

Diamond, 2011, Galván et al., 2012). Concentration dependencies were excluded from

234

modelling due to elemental concentrations of FBOM being extremely diluted, as a result of

235

the presence of inorganic matter (i.e. sediment) in FBOM samples. Organic proportions of

236

FBOM were likely ~ 3 % of total weight and using concentration dependencies would have

237

artificially inflated the contribution by a factor of ~ 40. POM was excluded from analysis as 8

238

the quantities obtained were insufficient to determine the sulfur stable isotope composition

239

for this source and the models are unable to run a combination of 2- and 3-isotope sources. A

240

combination of Gelman and Geweke diagnostics were used to ensure the model converged

241

adequately and that further simulations were not required. Model convergence was indicated

242

by Gelman confidence intervals being close to 1 and less than 1.1, and Geweke diagnostics,

243

which are a standard z-score calculated for each Monte Carlo Markov Chain (MCMC),

244

having ≤ 5 % of variables in each chain outside of ± 1.96 (Stock and Semmens, 2016). Only

245

providing the mean contribution with standard deviation of potential sources may hide multi-

246

modality or the extent of variation within prawn diet, reflecting variations in dietary

247

preference as well as availability (Semmens et al., 2013), and consequently posterior density

248

distributions were calculated.

249 250

3. Results

251

3.1 Isotopic composition of prawns and primary producers within Brisbane Water

252

Across all sites 24 School Prawns and 16 Eastern King Prawns were collected (n = 40) for

253

analysis. The isotopic signatures of primary producers were generally well separated in

254

isotopic space (Fig. 2), and the patterns across sites were broadly similar. At all sites, prawn

255

δ13C and δ34S values were clustered around S. virginicus (Fig. 2), while their δ15N were more

256

enriched than all sources (Fig. 3). Zostera muelleri was the most enriched δ13C source at all

257

sites (– 13.2 to – 10.5 ‰), except site 3, where Zostera epiphytes were the most enriched (–

258

7.09 ‰). FBOM was the most depleted δ34S source at all sites (– 0 .8 to – 32.6 ‰), with the

259

exception of site 2 where Zostera epiphytes were the most depleted (– 5.6 ‰). Within each

260

site δ15N signatures were relatively constrained, varying by ~ 2 – 4 ‰ (Fig. 3). Analysis of

261

variance indicated that A. marina and J. kraussi had similar isotopic compositions (i.e. were

262

not significantly different; Supplementary Material Table 1) at site 2 and 3 and were

263

subsequently grouped for analysis (‘A. marina + J. kraussi’). At site 4 A. marina, MPE, S.

264

quinqueflora and J. kraussi had similar isotopic compositions with regards to δ13C and δ34S,

265

however, their δ15N signatures were found to be different, despite this they were grouped,

266

giving preference to reducing the number of sources in the model (‘A. marina + others’;

267

Supplementary Material Table 1). This grouping was deemed appropriate given the relatively

268

narrow range of δ15N values for these sources, furthermore Phillips et al. (2014) suggest that

269

given sufficient knowledge of the system (or similar systems) and the appropriate iso-space

270

geometry such decisions can be appropriate.

271 9

272

3.2 Contribution of primary producers to penaeid nutrition

273

It was not possible to decrease the number of sources to n + 1 (where n is the number of

274

isotope tracers) based on isotopic similarity, consequently the contribution of each source

275

should be considered relative to others in our models. Furthermore, minor contributions

276

should be interpreted with caution as models with n + >1 overestimate the contributions of

277

these sources (Brett, 2014). The point-in-polygon simulation indicated that all prawns

278

sampled exhibited an isotopic signature that can be explained by the proposed mixing models

279

(i.e. the specified source means and standard deviations; Smith et al., 2013; Supplementary

280

Material Table 4). Patterns of source contributions were broadly similar for both prawn

281

species. Sporobolus virginicus was found to make contributions ranging from 8 ± 8 – 53 ± 16

282

% and was the dominant contributor for Eastern King Prawns and School Prawns at site 1,

283

and School Prawns at site 4, while Z. muelleri was found to make contributions over a similar

284

range for both species – 11 ± 10 – 47 ± 18 %, and was estimated to be the dominant

285

contributor at site 3 for both species and for Eastern King Prawns at site 4. Zostera epiphytes

286

were found to make high contributions to be the dominant contributor for both Eastern King

287

Prawns (51 ± 10 %) and School Prawns (53 ± 11 %) at site 2 and generally had lower

288

contributions at other sites (Fig. 4 & 5; Table 1). At sites where Z. muelleri or its epiphytes

289

were the dominant contributor a saltmarsh species was generally the next highest contributor

290

(e.g. S quinqueflora at site 2; S. australis for School Prawns at site 3; and S. virginicus for

291

Eastern King Prawns at site 4) similarly Z. muelleri was generally the second highest

292

contributing primary producer where a saltmarsh species (i.e. S. virginicus) was the dominant

293

producer (Table 1, Fig. 4 & 5). At all sites confidence intervals of all Gelman diagnostics

294

were < 1.05, and Geweke diagnostics were ≤ 5 % ± 1.96, except site 3 (where the second

295

chain had 8.3 % of variables ± 1.96). Despite site 3 not meeting the formal conditions

296

imposed by Geweke diagnostics longer simulations are unlikely to improve convergence here

297

, given that this was an ‘extreme’ length simulation (see Stock and Semmens, 2016), and the

298

model was deemed to have converged given that diagnostic outputs were broadly similar

299

among MCMCs, suggesting that longer Bayesian simulations were not necessary. Across all

300

sites, for both species, posterior density distributions were generally left-skewed (high

301

probability of low contribution) and highly constrained with the exception of S.virginicus and

302

Z. muelleri, which were either right-skewed (high probability of high contribution) or spread

303

over a higher range of proportional contributions (Fig. 4 & 5) suggesting some variability

304

within the diet of those populations. One exception to this pattern was School Prawns at site 4

305

which have a highly bimodal posterior density distribution with peaks at approximately 5 % 10

306

and 95 % contribution (Fig. 5d) possibly as a result of the a greater variability in consumer

307

δ15N (Fig. 3d).

308 309

4. Discussion

310

This study presents data from a temperate south-east Australian estuary indicating that a

311

range of primary producers are important for the nutrition of two species of penaeid prawn,

312

the School Prawn and Eastern King Prawn. In all cases either the saltmarsh grass

313

S. virginicus, seagrass Z. muelleri, or its associated epiphytes were found to be the dominant

314

source of nutrition for both species. In some instances these sources contributed over half of

315

the prawns diet, and where these sources were not found to be the dominant source they were

316

generally the second highest contributor. While there is some uncertainty regarding the exact

317

proportions contributed by these sources, the results presented here suggest that these two

318

habitats – saltmarsh and seagrass – are important components of the seascape nursery for

319

these species (Nagelkerken et al., 2015). These findings are significant as they highlight a

320

clear link between estuarine habitats and exploited species that support estuarine fisheries.

321 322

The importance of vegetated estuarine habitats to penaeid productivity is recognised across a

323

broad range of geographic regions (Boesch and Turner, 1984). For example, tropical and sub-

324

tropical regions seagrass (Embely River, Queensland; Loneragan et al., 1997) and mangroves

325

provide significant proportions of penaeid nutrition, however the provision of mangrove-

326

derived nutrition appears to be highly localised (Malaysia; Chong et al., 2001). In the absence

327

of seagrass, terrestrial saltmarsh species, such as Spartina alterniflora, are an important

328

source of nutrition for the Brown Shrimp, P. aztecus, in natural and restored saltmarsh

329

systems alike (Nueces Bay, Texas; Rezek et al., 2017). Similarly in Australian systems where

330

seagrass is not present, the saltmarsh grass, S. virginicus is the dominant source of nutrition

331

for a range of penaeid species. In the Hunter and Clarence River it is the dominant source of

332

nutrition for School Prawn and Eastern King Prawn (Taylor et al., 2017a, Raoult et al., 2018)

333

and in the Ross River (Queensland; Abrantes and Sheaves, 2009) it also makes important

334

contributions to the diet of other juvenile penaeid prawns (e.g. P. monodon, P. merguiensis

335

and M. bennettae). The importance of saltmarsh- and seagrass-derived nutrition where the

336

other habitat is lacking, suggests that these systems may be filling a productivity niche

337

created by the absence of the other (Ricklefs, 2010). Studies conducted in systems where

338

seagrass and saltmarsh are both present have resulted in ambiguous conclusions regarding the 11

339

contributions made by each habitat due to similarities in their δ13C values. For example,

340

Melville and Connolly (2005) could not separate between seagrass and S. virginicus in a

341

subtropical estuary in Queensland (Moreton Bay) on the basis of δ13C alone, and concluded

342

that high contributions of saltmarsh are unlikely due to their position in the intertidal zone,

343

and relatively small areal coverage when compared with seagrass. Other studies that have

344

encountered this issue have amalgamated these sources (Melville and Connolly, 2005,

345

Connolly and Waltham, 2015), confounding interpretations of the role of each of these

346

habitats.

347 348

Recently, studies have employed δ34S as a way to overcome the issue of similarity of isotope

349

ratios of sources (such as seagrass and saltmarsh grass, e.g. Connolly et al., 2004, or benthic

350

and pelagic sources, e.g. Duffill Telsnig et al., 2019), given that these producers typically

351

obtain their inorganic sulfur from isotopically distinct sources (Fry et al., 1982). The

352

usefulness of sulfur as an additional isotope tracer in this study is derived as a combination of

353

the differences in the isotopic composition of the sources sampled and from the additional

354

dimension (i.e. bivariate becomes multivariate isotopic space) that is added to the analysis.

355

This addition increases the surface area of the mixing polygon (i.e. the region contained

356

within each of the sources sampled, see Smith et al., 2013), which constrains model bias for

357

sources that are isotopically similar to the dominant contributor (Brett, 2014) allowing for

358

more accurate predictions to be made. For example, in other systems FBOM has been

359

estimated to make important contributions to the nutrition of both School Prawn and Eastern

360

King Prawn (Taylor et al., 2017a, Raoult et al., 2018). However, the isotope values for both

361

FBOM and the highest contributing source, S. virginicus, in these studies were relatively

362

similar. The results obtained here may indicate that the high contributions estimated in these

363

studies were an artefact of model bias (see Brett, 2014 for discussion), which was removed

364

via the inclusion of sulfur as an additional isotope tracer in this study, thus improving model

365

performance and allowing for adequate differentiation of the dominant contributors in this

366

system – Z. muelleri its associated epiphytes, and S. virginicus. The similarly high

367

contributions of these sources across the estuary provide good evidence that both saltmarsh-

368

and seagrass-derived material represent important sources of nutrition for prawns.

369

Furthermore, these findings highlight S. virginicus as a highly important source of nutrition in

370

seagrass-limited and seagrass-dominated systems alike.

371

12

372

Stable isotope analysis, and more specifically Bayesian mixing models, are not without

373

limitations. Even when adhering to ‘best-practices’ for the use of stable isotope mixing

374

models (Phillips et al., 2014) it is still possible to obtain biased or misleading results. For

375

example, the study design employed here (i.e. only sampling prawns over seagrass beds)

376

could introduce bias in our results, however we argue that the high-mobility of prawns

377

coupled with the use of muscle tissue in our analysis, which is an indicator of medium-long

378

term diet (Hewitt et al., 2018), nullify this concern. Furthermore, the sample size for

379

consumers employed here (≥ 3) represents the lower boundary considered appropriate for

380

modelling. It should be noted that such sample sizes can lead to highly diffuse source

381

contributions (Brett, 2014, Phillips et al., 2014) – such as the highly variable and bimodal

382

contribution (i.e. either large or small contribution) of S. virginicus to the diet of School

383

Prawn at site 4 presented here. Increasing the sample size would likely see an increase in the

384

accuracy of the estimates obtained here, and we recommend this for future studies (Pearson

385

and Grove, 2013). Sample preparation techniques, such as acidification to remove calcareous

386

material, can also alter the stable isotope signatures of sources and consumers, thereby

387

influencing model outputs (Schlacher and Connolly, 2014). While all effort was taken to

388

remove calcareous material from samples such as FBOM and epiphytes (both seagrass and

389

mangrove), it is possible that some of this material may have been present in the final sample,

390

ultimately affecting model outputs. We note that the effect of acidification is generally to

391

deplete the δ13C and δ15N values of samples (Schlacher and Connolly, 2014) and in all cases

392

this would have decreased the contributions of these sources, however the mean magnitude of

393

change (0.68 ± 0.12 ‰ for δ13C and 0.16 ± 0.06 ‰ for δ15N) are not of an order great enough

394

to change the relative contributions presented here (Schlacher and Connolly, 2014).The

395

combination of these factors suggest that the results presented here should be interpreted with

396

caution. However, given that these results are consistent with other sites within the estuary,

397

and those reported elsewhere (i.e. the Clarence and Hunter River; Raoult et al., 2018, Taylor

398

et al., 2017a, Taylor et al., 2017b), we remain confident in these findings and place emphasis

399

on the ordering of source contirbutions rather than precise estimates. Finally, the application

400

of inappropriate TEFs or the omission of a possible source can confound model outputs,

401

however, we are satisfied that all possible sources were included and that the TEFs used for

402

analysis were adequate given the result of our point-in-polygon simulations (Smith et al.,

403

2013).

404

13

405

In estuaries, trophic subsidy can vary over several spatial scales, from a few metres (Guest

406

and Connolly, 2004) to several kilometres (Gaston et al., 2006). As a result, organisms derive

407

their nutrition from a combination of local and spatially separate habitats. While there is

408

evidence to suggest that saltmarsh have little impact on diet beyond their borders (Hyndes et

409

al., 2014), the high contributions of S. virginicus presented here suggest that they may be

410

subsidising estuarine food webs well beyond their borders, especially given that penaeid

411

prawns have been shown to exhibit relatively low levels of direct saltmarsh interaction

412

(Becker and Taylor, 2017). However, the mechanisms by which this subsidy occur are poorly

413

understood. Saltmarsh primary productivity (1.38 kg C m-2 year-1) is much higher than other

414

estuarine habitats (i.e. seagrass, 0.46 kg C m-2 year-1; Hyndes et al., 2014), with S. virginicus

415

being one of the most productive of all saltmarsh species (Linthurst and Reimold, 1978).

416

Other studies suggest that high proportions of this productivity are exported out of these

417

saltmarsh habitats through tidal transport (Taylor and Allanson, 1995) before being processed

418

by benthic and epibenthic organisms (Svensson et al., 2007). However, it is unlikely that tidal

419

transport alone fully accounts for such high contributions of S. virginicus given the relatively

420

high position of saltmarsh habitats within the intertidal zone, and their infrequent inundation,

421

in south-east Australia. Furthermore, tidal attenuation in Brisbane Water is generally

422

proportional to distance from the mouth. Contributions of S. virginicus (and other saltmarsh

423

species) were consistent across the length of the estuary; if tidal transport were the sole

424

mechanism of contribution, then under these conditions we would expect to see decreasing

425

contributions of S. virginicus (and other saltmarsh species) with increasing distance from the

426

mouth. Direct consumption, and subsequent transport of saltmarsh material by benthic and

427

epibenthic consumers, such as mysids (Fockedey and Mees, 1999, Svensson et al., 2007),

428

may represent another avenue by which saltmarsh contribute to prawn diet, as they are known

429

to have a varied diet of plant material, detritus, crustaceans, microorganisms, small shellfish

430

and worms (Racek, 1959, Moriarty, 1977). The combination of high saltmarsh productivity

431

and multiple potential avenues of export provide a plausible theoretical framework through

432

which saltmarsh can contribute to the diet of prawns.

433 434

The findings here suggest that both saltmarsh and seagrass habitats represent important

435

resources supporting the productivity of prawns in Brisbane Water, while other habitats such

436

as mangroves appear to make much lower contributions. Like many other estuarine systems,

437

Brisbane Water (and its catchment) has undergone significant development since European

438

settlement (Harty and Cheng, 2003, Cardno Lawson Treolar, 2008). This has resulted in 14

439

widespread habitat loss, primarily through land reclamation to make way for urban and

440

residential development. Intertidal habitats, such as saltmarsh have been disproportionately

441

affected by such impacts, and over the period from 1954 – 1995 losses of ~ 78 % (183 ha) of

442

saltmarsh habitat have been recorded in the Brisbane Water catchment (Harty and Cheng,

443

2003). Conversely, seagrass coverage has increased by 8 % (42 ha) over the period 1985 –

444

2006 (Jelbart and Ross, 2006). Anthropogenic impacts to seagrass may have manifested

445

themselves as a compositional change, whereby an increase in Z. muelleri (62 – 74 %) was

446

accompanied by a decrease in the extent of P. australis (43 – 47 %). It is likely that the losses

447

to saltmarsh habitat have occurred in areas important for prawns, and had subsequent

448

negative effects on prawn productivity across the estuary, however the increase in area of

449

seagrass, specifically Z. muelleri, may have offset such losses to some degree.

450 451

The repair and restoration of saltmarsh habitats via reinstatement of tidal flow represent a

452

possible management action that is likely to have positive impacts for the productivity of

453

several estuarine species (Boys et al., 2012). Estimates of the potential economic benefits of

454

habitat repair range from AUD $1, 251 ha-1 to AUD $5, 175 ha-1 annually (depending on

455

recruitment; Taylor and Creighton, 2018). These estimates represent benefits derived from

456

the School Prawn fishery in the Clarence River estuary, and do not account for potential

457

flow-on effects, such as increased productivity of other species or subsidy of adjacent

458

fisheries, likely to arise from such habitat repair. While Brisbane Water does not support a

459

direct harvest commercial prawn fishery, the adjacent Hawkesbury River Prawn Trawl is

460

likely to be a key beneficiary of habitat repair within the Brisbane Water catchment.

461

Furthermore, recreational fisheries represent a highly important industry with Brisbane Water

462

that are also likely to realise the benefits of targeted habitat repair. These examples highlight

463

how management actions such as reinstatement of tidal flow (and connectivity) can result in

464

beneficial outcomes for commercially important species and the fisheries they support.

465 466

5. Conclusions

467

Stable isotopes have been widely employed to reconstruct estuarine food webs (Melville and

468

Connolly, 2003, Fry, 2006). Despite such broad application some uncertainty regarding the

469

relative contributions of various habitats have remained (i.e. seagrass and saltmarsh; Melville

470

and Connolly, 2005). Recent work has indicated that penaeid prawns rely on saltmarsh-

471

derived material, however, these results reflect food-web dynamics in seagrass-limited

472

systems (Taylor et al., 2017a, Raoult et al., 2018). The results presented here suggest that 15

473

both saltmarsh- and seagrass-derived nutrition are important for the productivity of Eastern

474

King Prawn and School Prawn populations, and that the variability in contributions made by

475

these sources across studies (and geographic locations) is a result of the differential

476

availability of these sources. These findings have implications for the repair of saltmarsh

477

habitats, which have undergone significant degradation over decadal time scales (Rogers et

478

al., 2015). Reinstatement of tidal flow, and the subsequent restoration of connectivity across

479

habitats within estuaries, is likely to have beneficial ecological outcomes for penaeid prawns

480

and the commercial and recreational fisheries they support are likely to be the key

481

beneficiaries of such management actions (Creighton et al., 2015).

482 483

6. References

484

ABRANTES, K., BARNETT, A., BAKER, R. & SHEAVES, M. 2015a. Habitat-specific

485

food webs and trophic interactions supporting coastal-dependent fishery species: an

486

Australian case study. Reviews in Fish Biology and Fisheries, 25, 337-363.

487

ABRANTES, K., BARNETT, A., BAKER, R. & SHEAVES, M. 2015b. Habitat-specific

488

food webs and trophic interactions supporting coastal-dependent fishery species: an

489

Australian case study. Reviews in Fish Biology and Fisheries, 25, 337-363.

490

ABRANTES, K. & SHEAVES, M. 2009. Sources of nutrition supporting juvenile penaeid

491

prawns in an Australian dry tropics estuary. Marine and Freshwater Research, 60,

492

949-959.

493

BARBIER, E. B., HACKER, S. D., KENNEDY, C., KOCH, E. W., STIER, A. C. &

494

SILLIMAN, B. R. 2011. The value of estuarine and coastal ecosystem services.

495

Ecological Monographs, 81, 169-193.

496

BECK, M. W., HECK, K. L., ABLE, K. W., CHILDERS, D. L., EGGLESTON, D. B.,

497

GILLANDERS, B. M., HALPERN, B., HAYS, C. G., HOSHINO, K., MINELLO, T.

498

J., ORTH, R. J., SHERIDAN, P. F. & WEINSTEIN, M. R. 2001. The identification,

499

conservation, and management of estuarine and marine nurseries for fish and

500

invertebrates. Bioscience, 51, 633-641.

501

BECKER, A. & TAYLOR, M. D. 2017. Nocturnal sampling reveals usage patterns of

502

intertidal marsh and subtidal creeks by penaeid shrimp and other nekton in south-

503

eastern Australia. Marine and Freshwater Research, 68, 780-787.

504 505

BOESCH, D. F. & TURNER, R. E. 1984. Dependence of fishery species on salt marshes the role of food and refuge. Estuaries, 7, 460-468. 16

506

BOND, A. L. & DIAMOND, A. W. 2011. Recent Bayesian stable‐isotope mixing models

507

are highly sensitive to variation in discrimination factors. Ecological Applications, 21,

508

1017-1023.

509 510

BOUILLON, S., CONNOLLY, R. & GILLIKIN, D. 2011. Use of stable isotopes to understand food webs and ecosystem functioning in estuaries.

511

BOYS, C. A., KROON, F. J., GLASBY, T. M. & WILKINSON, K. 2012. Improved fish and

512

crustacean passage in tidal creeks following floodgate remediation. Journal of

513

Applied Ecology, 49, 223-233.

514 515

BRETT, M. T. J. M. E. P. S. 2014. Resource polygon geometry predicts Bayesian stable isotope mixing model bias. 514, 1-12.

516

CARDNO LAWSON TREOLAR 2008. Brisbane Water estuary processes study. Prepared

517

for Gosford City Council and Department of Environment and Climate Change.

518

CAUT, S., ANGULO, E. & COURCHAMP, F. 2009. Variation in discrimination factors

519

(∆15N and ∆13C): the effect of diet isotopic values and applications for diet

520

reconstruction. Journal of Applied Ecology, 46, 443-453.

521

CHONG, V. C., LOW, C. B. & ICHIKAWA, T. 2001. Contribution of mangrove detritus to

522

juvenile prawn nutrition: a dual stable isotope study in a Malaysian mangrove forest.

523

Marine Biology, 138, 77-86.

524

CONNOLLY, R. M., GUEST, M. A., MELVILLE, A. J. & OAKES, J. M. 2004. Sulfur

525

stable isotopes separate producers in marine food-web analysis. Oecologia, 138, 161-

526

7.

527 528

CONNOLLY, R. M. & WALTHAM, N. J. 2015. Spatial analysis of carbon isotopes reveals seagrass contribution to fishery food web. Ecosphere, 6, art148.

529

COSTANZA, R., DARGE, R., DEGROOT, R., FARBER, S., GRASSO, M., HANNON, B.,

530

LIMBURG, K., NAEEM, S., ONEILL, R. V., PARUELO, J., RASKIN, R. G.,

531

SUTTON, P. & VANDENBELT, M. 1997. The value of the world's ecosystem

532

services and natural capital. Nature, 387, 253-260.

533

CREIGHTON, C., BOON, P. I., BROOKES, J. D. & SHEAVES, M. 2015. Repairing

534

Australia's estuaries for improved fisheries production – what benefits, at what cost?

535

Marine and Freshwater Research, 66, 493-507.

536

CURRIN, C. A., LEVIN, L. A., TALLEY, T. S., MICHENER, R. & TALLEY, D. 2011. The

537

role of cyanobacteria in Southern California salt marsh food webs. Marine Ecology,

538

32, 346-363.

17

539 540

DALL, W., HILL, B. J., ROTHLISBERG, P. C. & STAPLES, D. J. 1990. The Biology of the Penaeidae. Advances in Marine Biology, 27, 1-461.

541

DE ABREU, D. C., PAULA, J. & MACIA, A. 2017. Tropical seascapes as feeding grounds

542

for juvenile penaeid shrimps in southern Mozambique revealed using stable isotopes.

543

Estuarine, Coastal and Shelf Science, 198, 21-28.

544

DITTEL, A., EPIFANIO, C., SCHWALM, S., FANTLE, M. S. & FOGEL, M. 2000. Carbon

545

and nitrogen sources for juvenile blue crabs Callinectes sapidus in coastal wetlands.

546

Marine Ecology Progress Series, 194, 103-112.

547

DUFFILL TELSNIG, J. I., JENNINGS, S., MILL, A. C., WALKER, N. D., PARNELL, A.

548

C. & POLUNIN, N. V. C. 2019. Estimating contributions of pelagic and benthic

549

pathways to consumer production in coupled marine food webs. Journal of Animal

550

Ecology, 88, 405-415.

551

FENG, J. X., GAO, Q. F., DONG, S. L., SUN, Z. L. & ZHANG, K. 2014. Trophic

552

relationships in a polyculture pond based on carbon and nitrogen stable isotope

553

analyses: A case study in Jinghai Bay, China. Aquaculture, 428, 258-264.

554

FOCKEDEY, N. & MEES, J. 1999. Feeding of the hyperbenthic mysid Neomysis integer in

555

the maximum turbidity zone of the Elbe, Westerschelde and Gironde estuaries.

556

Journal of Marine Systems, 22, 207-228.

557

FORD, J., FOWLER, A. & SUTHERS, A. 2006. Brisbane Water estuary study: Larval fish

558

settlement, zooplankton and phytoplankton, during spring 2005. Prepared for Gosford

559

City Council.

560

FRY, B. 2006. Stable isotope ecology, Springer.

561

FRY, B. 2008. Open bays as nurseries for Louisiana Brown Shrimp. Estuaries and Coasts,

562

31, 776-789.

563

FRY, B., SCALAN, R. S., WINTERS, J. K. & PARKER, P. L. 1982. Sulfur uptake by salt

564

grasses, mangroves, and seagrasses in anaerobic sediments. Geochimica Et

565

Cosmochimica Acta, 46, 1121-1124.

566 567

GALVÁN, D., SWEETING, C. & POLUNIN, N. 2012. Methodological uncertainty in resource mixing models for generalist fishes. Oecologia, 169, 1083-93.

568

GASTON, T. F., SCHLACHER, T. A. & CONNOLLY, R. M. 2006. Flood discharges of a

569

small river into open coastal waters: Plume traits and material fate. Estuarine Coastal

570

and Shelf Science, 69, 4-9.

18

571

GUEST, M. A. & CONNOLLY, R. M. 2004. Fine-scale movement and assimilation of

572

carbon in saltmarsh and mangrove habitat by resident animals. Aquatic Ecology, 38,

573

599-609.

574

HAAS, H. L., ROSE, K. A., FRY, B., MINELLO, T. J. & ROZAS, L. P. 2004. Brown

575

shrimp on the edge: Linking habitat to survival using an individual-based simulation

576

model. Ecological Applications, 14, 1232-1247.

577

HARTY, C. & CHENG, D. 2003. Ecological assessment and strategies for the management

578

of mangroves in Brisbane Water—Gosford, New South Wales, Australia. Landscape

579

and Urban Planning, 62, 219-240.

580

HEWITT, D. E., SMITH, T. M., RAOULT, V., TAYLOR, M. D. & GASTON, T. F. 2018.

581

Multiple stable isotopes indicate the importance of the saltmarsh grass Sporobolus

582

virginicus to the diet of two commercially important penaeid species in a temperate

583

south-east Australian estuary. Bachelor of Environmental Science and Management

584

(Honours), University of Newcastle.

585

HINDELL, J. S. & WARRY, F. Y. 2010. Nutritional support of estuary perch (Macquaria

586

colonorum) in a temperate Australian inlet: Evaluating the relative importance of

587

invasive Spartina. Estuarine Coastal and Shelf Science, 90, 159-167.

588 589

HOBSON, K. A. 1999. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia, 120, 314-326.

590

HYNDES, G. A., NAGELKERKEN, I., MCLEOD, R. J., CONNOLLY, R. M., LAVERY, P.

591

S. & VANDERKLIFT, M. A. 2014. Mechanisms and ecological role of carbon

592

transfer within coastal seascapes. Biological Reviews, 89, 232-254.

593

JELBART, J. E. & ROSS, P. M. 2006. Examination of the seagrass and associated fauna in

594

the Gosford local government area (GLGA) including Correa Bay and Patonga Creek.

595

Prepared for Gosford City Council.

596 597

KENNISH, M. J. 2002. Environmental threats and environmental future of estuaries. Environmental Conservation, 29, 78-107.

598

KNEIB, R. 2000. Saltmarsh ecoscapes and production transfers by estuarine nekton in the

599

southeastern United States. In: WEINSTEIN, M. & KREEGER, D. (eds.) Concepts

600

and controversies in tidal marsh ecology. Dordrecht: Kluwer Academic.

601

LINTHURST, R. A. & REIMOLD, R. J. 1978. Estimated net aerial primary productivity for

602

selected estuarine angiosperms in Maine, Delaware, and Georgia. Ecology, 59, 945-

603

955.

19

604

LONERAGAN, N. R., BUNN, S. E. & KELLAWAY, D. M. 1997. Are mangroves and

605

seagrasses sources of organic carbon for penaeid prawns in a tropical Australian

606

estuary? A multiple stable-isotope study. Marine Biology, 130, 289-300.

607

LONERAGAN, N. R., KANGAS, M., HAYWOOD, M. D. E., KENYON, R. A., CAPUTI,

608

N. & SPORER, E. 2013. Impact of cyclones and aquatic macrophytes on recruitment

609

and landings of tiger prawns Penaeus esculentus in Exmouth Gulf, Western Australia.

610

Estuarine Coastal and Shelf Science, 127, 46-58.

611

MAZUMDER, D., ILES, J., KELLEWAY, J., KOBAYASHI, T., KNOWLES, L.,

612

SAINTILAN, N. & HOLLINS, S. 2010. Effect of acidification on elemental and

613

isotopic compositions of sediment organic matter and macro-invertebrate muscle

614

tissues in food web research. Rapid Communications in Mass Spectrometry, 24, 2938-

615

2942.

616

MAZUMDER, D., WILLIAMS, R. J., REID, D., SAINTILAN, N. & SZYMCZAK, R. 2008.

617

Variability of stable isotope ratios of Glassfish (Ambassis jacksoniensis) from

618

mangrove/saltmarsh environments in southeast Australia and implications for

619

choosing sample size. Environmental Bioindicators, 3, 114-123.

620

MCCUTCHAN, J. H., LEWIS, W. M., KENDALL, C. & MCGRATH, C. C. 2003. Variation

621

in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos, 102,

622

378-390.

623 624

MELVILLE, A. J. & CONNOLLY, R. M. 2003. Spatial analysis of stable isotope data to determine primary sources of nutrition for fish. Oecologia, 136, 499-507.

625

MELVILLE, A. J. & CONNOLLY, R. M. 2005. Food webs supporting fish over subtropical

626

mudflats are based on transported organic matter not in situ microalgae. Marine

627

Biology, 148, 363-371.

628

MINELLO, T. J., ABLE, K. W., WEINSTEIN, M. P. & HAYS, C. G. 2003. Salt marshes as

629

nurseries for nekton: testing hypotheses on density, growth and survival through

630

meta-analysis. Marine Ecology Progress Series, 246, 39-59.

631 632 633 634

MOBSBY, D. & KODUAH, A. 2017. Australian fisheries and aquaculture statistics 2016. In: CORPORATION, F. R. A. D. (ed.). Canberra, ACT: ABARES. MORIARTY, D. 1977. Quantification of carbon, nitrogen and bacterial biomass in the food of some penaeid prawns. Marine and Freshwater Research, 28, 113-118.

635

NAGELKERKEN, I., SHEAVES, M., BAKER, R. & CONNOLLY, R. M. 2015. The

636

seascape nursery: a novel spatial approach to identify and manage nurseries for

637

coastal marine fauna. Fish and Fisheries, 16, 362-371. 20

638

NATIONAL LAND AND WATER RESOURCES AUDIT 2002. NLWRA 1997-2002 Final

639

Report. In: LAND AND WATER AUSTRALIA - AUSTRALIAN GOVERNMENT

640

OF AGRICULTURE, F. A. F. (ed.). Canberra, ACT.

641

OCHWADA, F., LONERAGAN, N. R., GRAY, C. A., SUTHERS, I. M. & TAYLOR, M. D.

642

2009. Complexity affects habitat preference and predation mortality in postlarval

643

Penaeus plebejus: implications for stock enhancement. Marine Ecology Progress

644

Series, 380, 161-171.

645

ODUM, E. 2000. Tidal marshes as outwelling/pulsing ecosystems. In: WEINSTEIN, M. &

646

KREEGER, D. (eds.) Concepts and controversies in tidal marsh ecology. Dordrecht:

647

Kluwer Academic.

648 649

PARNELL, A. C., INGER, R., BEARHOP, S. & JACKSON, A. L. 2010. Source partitioning using stable isotopes: Coping with too much variation. Plos One, 5, e9672.

650

PARNELL, A. C., PHILLIPS, D. L., BEARHOP, S., SEMMENS, B. X., WARD, E. J.,

651

MOORE, J. W., JACKSON, A. L., GREY, J., KELLY, D. J. & INGER, R. 2013.

652

Bayesian stable isotope mixing models. Environmetrics, 24, 387-399.

653

PEARSON, J. & GROVE, M. 2013. Counting sheep: sample size and statistical inference in

654

stable isotope analysis and palaeodietary reconstruction. World Archaeology, 45, 373-

655

387.

656

PETERSON, B. J., HOWARTH, R. W. & GARRITT, R. H. 1985. Multiple stable isotopes

657

used to trace the flow of oganic matter in estuarine food webs. Science, 227, 1361-

658

1363.

659 660

PHILLIPS, D. L. & GREGG, J. W. 2003. Source partitioning using stable isotopes: coping with too many sources. Oecologia, 136, 261-269.

661

PHILLIPS, D. L., INGER, R., BEARHOP, S., JACKSON, A. L., MOORE, J. W.,

662

PARNELL, A. C., SEMMENS, B. X. & WARD, E. J. 2014. Best practices for use of

663

stable isotope mixing models in food-web studies. Canadian Journal of Zoology, 92,

664

823-835.

665

PRIMAVERA, J. H. 1996. Stable carbon and nitrogen isotope ratios of penaeid juveniles and

666

primary producers in a riverine mangrove in Guimaras, Philippines. Bulletin of

667

Marine Science, 58, 675-683.

668

QUAN, W., FU, C., JIN, B., LUO, Y., LI, B., CHEN, J. & WU, J. 2007. Tidal marshes as

669

energy sources for commercially important nektonic organisms: stable isotope

670

analysis. Marine Ecology Progress Series, 352, 89-99.

21

671 672 673 674

R DEVELOPMENT CORE TEAM 2013. R: A language and environment for statistical computing. Vienna Austria: R Foundation for Statistical Computing. RACEK, A. A. 1959. Prawn investigations in eastern Australia, Government Printer, South Africa.

675

RAOULT, V., GASTON, T. F. & TAYLOR, M. D. 2018. Habitat–fishery linkages in two

676

major south-eastern Australian estuaries show that the C4 saltmarsh plant Sporobolus

677

virginicus is a significant contributor to fisheries productivity. Hydrobiologia, 811,

678

221-238.

679

REZEK, R. J., LEBRETON, B., STERBA-BOATWRIGHT, B. & BESERES POLLACK, J.

680

2017. Ecological structure and function in a restored versus natural salt marsh. PLOS

681

ONE, 12, e0189871.

682

RICKLEFS, R. E. 2010. Evolutionary diversification, coevolution between populations and

683

their antagonists, and the filling of niche space. Proceedings of the National Academy

684

of Sciences of the United States of America, 107, 1265-1272.

685

ROGERS, K., KNOLL, E. J., COPELAND, C. & WALSH, S. 2015. Quantifying changes to

686

historic fish habitat extent on north coast NSW floodplains, Australia. Regional

687

Environmental Change, 16, 1469-1479.

688

ROY, P. S., WILLIAMS, R. J., JONES, A. R., YASSINI, I., GIBBS, P. J., COATES, B.,

689

WEST, R. J., SCANES, P. R., HUDSON, J. P. & NICHOL, S. 2001. Structure and

690

function of south-east Australian estuaries. Estuarine Coastal and Shelf Science, 53,

691

351-384.

692

SAINTILAN, N. & MAZUMDER, D. 2010. Fine-scale variability in the dietary sources of

693

grazing invertebrates in a temperate Australian saltmarsh. Marine and Freshwater

694

Research, 61, 615-620.

695

SAINTILAN, N., ROGERS, K., MAZUMDER, D. & WOODROFFE, C. 2013.

696

Allochthonous and autochthonous contributions to carbon accumulation and carbon

697

store in southeastern Australian coastal wetlands. Estuarine Coastal and Shelf

698

Science, 128, 84-92.

699 700

SAINTILAN, N. & WEN, L. 2012. Environmental predictors of estuarine fish landings along a temperate coastline. Estuarine Coastal and Shelf Science, 113, 221-230.

701

SCHLACHER, T. A. & CONNOLLY, R. M. 2014. Effects of acid treatment on carbon and

702

nitrogen stable isotope ratios in ecological samples: a review and synthesis. Methods

703

in Ecology and Evolution, 5, 541-550.

22

704

SEMMENS, B. X., WARD, E. J., PARNELL, A. C., PHILLIPS, D. L., BEARHOP, S.,

705

INGER, R., JACKSON, A. & MOORE, J. W. 2013. Statistical basis and outputs of

706

stable isotope mixing models: Comment on Fry (2013). Marine Ecology Progress

707

Series, 490, 285-289.

708

SMITH, J. A., MAZUMDER, D., SUTHERS, I. M. & TAYLOR, M. D. 2013. To fit or not to

709

fit: evaluating stable isotope mixing models using simulated mixing polygons.

710

Methods in Ecology and Evolution, 4, 612-618.

711

STOCK, B. C. & SEMMENS, B. X. 2016. MixSIAR GUI User Manual. 3.1 ed.

712

SUZUKI, K. W., KASAI, A., ISODA, T., NAKAYAMA, K. & TANAKA, M. 2008.

713

Distinctive stable isotope ratios in important zooplankton species in relation to

714

estuarine salinity gradients: Potential tracer of fish migration. Estuarine, Coastal and

715

Shelf Science, 78, 541-550.

716

SVENSSON, C. J., HYNDES, G. A. & LAVERY, P. S. 2007. Food web analysis in two

717

permanently open temperate estuaries: consequences of saltmarsh loss? Mar Environ

718

Res, 64, 286-304.

719

TAYLOR, D. I. & ALLANSON, B. R. 1995. Organic-carbon fluxes between a high marsh

720

and estuary, and the inapplicability of the outwelling hypothesis. Marine Ecology

721

Progress Series, 120, 263-270.

722 723

TAYLOR, M. D. 2016. Identifying and understanding nursery hbaitats for exploited penaeid shrimp in NSW estuaries. 25th Annual NSW Coastal Conference. Coffs Harbour.

724

TAYLOR, M. D., BECKER, A., MOLTSCHANIWSKYJ, N. A. & GASTON, T. F. 2017a.

725

Direct and indirect interactions between lower estuarine mangrove and saltmarsh

726

habitats and a commercially important penaeid shrimp. Estuaries and Coasts, 1-12.

727

TAYLOR, M. D. & CREIGHTON, C. 2018. Estimating the potential fishery benefits from

728

targeted habitat repair: a case study of School Prawn (Metapenaeus macleayi) in the

729

lower Clarence River estuary. Wetlands, 38, 1199-1209.

730

TAYLOR, M. D., FRY, B., BECKER, A. & MOLTSCHANIWSKYJ, N. 2017b. The role of

731

connectivity and physicochemical conditions in effective habitat of two exploited

732

penaeid species. Ecological Indicators, 80, 1-11.

733 734 735 736

TURNER, R. E. 1977. Intertidal vegetation and commercial yields of penaeid shrimp. Transactions of the American Fisheries Society, 106, 411-416. VANDERKLIFT, M. A. & PONSARD, S. 2003. Sources of variation in consumer-diet δ15N enrichment: a meta-analysis. Oecologia, 136, 169-182.

23

737

WAYCOTT, M., DUARTE, C. M., CARRUTHERS, T. J., ORTH, R. J., DENNISON, W.

738

C., OLYARNIK, S., CALLADINE, A., FOURQUREAN, J. W., HECK, K. L., JR.,

739

HUGHES, A. R., KENDRICK, G. A., KENWORTHY, W. J., SHORT, F. T. &

740

WILLIAMS, S. L. 2009. Accelerating loss of seagrasses across the globe threatens

741

coastal ecosystems. Proc Natl Acad Sci U S A, 106, 12377-81.

742

WILSON, R. M., CHANTON, J., LEWIS, F. G. & NOWACEK, D. 2010. Concentration-

743

dependent stable isotope analysis of consumers in the upper reaches of a freshwater-

744

dominated estuary: Apalachicola Bay, FL, USA. Estuaries and Coasts, 33, 1406-

745

1419.

746

WORM, B., BARBIER, E. B., BEAUMONT, N., DUFFY, J. E., FOLKE, C., HALPERN, B.

747

S., JACKSON, J. B., LOTZE, H. K., MICHELI, F., PALUMBI, S. R., SALA, E.,

748

SELKOE, K. A., STACHOWICZ, J. J. & WATSON, R. 2006. Impacts of

749

biodiversity loss on ocean ecosystem services. Science, 314, 787-90.

750

YOKOYAMA, H., TAMAKI, A., HARADA, K., SHIMODA, K., KOYAMA, K. & ISHIHI,

751

Y. 2005. Variability of diet-tissue isotopic fractionation in estuarine macrobenthos.

752

Marine Ecology Progress Series, 296, 115-128.

753

ZANDEN, M. & RASMUSSEN, J. B. 2001. Variation in δ15N and δ13C trophic fractionation:

754

implications for aquatic food web studies. Limnology and oceanography, 46, 2061-

755

2066.

756 757 758

ZIMMERMAN, R. J. & MINELLO, T. J. 1984. Densities of Penaeus aztecus, Penaeus setiferus, and other natant macrofauna in a Texas salt-marsh. Estuaries, 7, 421-433. Acknowledgements We thank T. Ryan for assistance with collection of samples throughout this project. This project was supported by the University of Newcastle Faculty of Science. Funding bodies and project partners had no role in the design, data collection, analysis or interpretation of data. Sampling was carried out under NSW Department of Primary Industries Scientific Collection permit P13/0037-1.3.

24

Figure 1: Map of the Brisbane Water estuary showing the distribution of seagrass (bright green), saltmarsh (orange) and mangrove (dark green) and sites sampled (▲; 1 – 4). Habitat data obtained from NSW Department of Primary Industries – Fisheries, Habitat Mapping Database.

25

Figure 2: Mean (± SD) carbon and sulfur stable isotope ratios for School Prawn (∆), Eastern King Prawn (○) and primary producers (■) across Brisbane Water for a site 1, b site 2, c site 3 and d site 4. ‘Zostera epiphytes’ are composite samples at each site. Site numbers correspond to Fig. 1. A. marina + others: mangrove leaves, MPE, S. quinqueflora and J. kraussi.

Figure 3: Mean (± SD) carbon and nitrogen stable isotope ratios for School Prawn (∆), Eastern King Prawn (○) and primary producers (■) across Brisbane Water for a site 1, b site 2, c site 3 and d site 4. ‘Zostera epiphytes’ are composite samples at each site. Site numbers correspond to Fig. 1. A. marina + others: mangrove leaves, MPE, S. quinqueflora and J. kraussi.

27

Figure 4: Posterior density distributions of proportion of contribution to diet of Eastern King Prawns from potential sources in Brisbane Water at a site 1, b site 2, c site 3 and d site 4, estimated using Bayesian mixing models. FBOM: fine benthic organic matter, MPE: mangrove pneumatophore epiphytes, A. marina + others: mangrove leaves, MPE, S. quinqueflora and J. kraussi. 28

Figure 5: Posterior density distributions of proportion of contribution to diet of School Prawns from potential sources in Brisbane Water at a site 1, b site 2, c site 3 and d site 4, estimated using Bayesian mixing models. FBOM: fine benthic organic matter, MPE: mangrove pneumatophore epiphytes, A. marina + others: mangrove leaves, MPE, S. quinqueflora and J. kraussi. 29

Table 1: Mean proportion (± SD) of contribution to diet of prawns by common estuarine primary producers in Brisbane Water, as predicted by Bayesian mixing models Site 1

2

3

4

Consumer

n

S. australis

S. quinqueflora

S. virginicus

Z. muelleri

Zostera epiphytes

Eastern King Prawn

4

-c

-b

0.518 (0.141)

0.225 (0.122)

-c

School Prawn

5

-c

-b

0.530 (0.166)

0.226 (0.148)

-c

Eastern King Prawn

8

-b

0.153 (0.090)

-b

0.126 (0.091)

0.512 (0.105)

School Prawn

5

-b

0.140 (0.097)

-b

0.118 (0.104)

0.538 (0.118)

Eastern King Prawn

7

0.106 (0.078)

-b

0.130 (0.201)

0.405 (0.204)

0.147 (0.149)

School Prawn

3

0.183 (0.090)

-b

0.105 (0.153)

0.478 (0.189)

0.103 (0.127)

Eastern King Prawn

5

-b

-d

0.292 (0.278)

0.367 (0.234)

0.120 (0.098)

School Prawn

3

-b

-d

0.526 (0.421) a

0.322 (0.320)

-c

Values in bold are the dominant contributor for that species. a

Source has bimodal posterior distribution (i.e. estimated contributions are either low or high; see Fig. 5d).

b

Sources with contributions less than 10 % have been omitted from this table, full output can be found in Supplementary Material Table 3.

c

Source not present at this site.

d

Source combined with others (based on isotopic similarity, see Methods).

30

759

Highlights •

Prawns exhibit several ontogenetic shifts in habitat use during their life cycle

•

Vegetated estuarine habitats act as nursery habitats during their juvenile phase

•

These habitats support fisheries productivity via the trophic support of species

•

Stable isotopes show saltmarsh contributes disproportionately to prawn nutrition

Conflict of Interest and Authorship Conformation Form Please check the following as appropriate:

X All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. X This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. X The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript X The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Author’s name Affiliation Daniel Hewitt, University of Newcastle (at time of research, currently University of New South Wales) Tim Smith, University of Newcastle Vincent Raoult, University of Newcastle Troy Gaston, University of Newcastle Matt Taylor, University of Newcastle, NSW Department of Primary Industries Fisheries

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: