Trophic model of a deep-sea ecosystem with methane seeps in the South China Sea

Journal Pre-proof Trophic model of a deep-sea ecosystem with methane seeps in the South China Sea Zhe-Yu Lin, Hsuan-Wien Chen, Hsing-Juh Lin PII:

S0967-0637(20)30039-X

DOI:

https://doi.org/10.1016/j.dsr.2020.103251

Reference:

DSRI 103251

To appear in:

Deep-Sea Research Part I

Received Date: 18 March 2019 Revised Date:

13 October 2019

Accepted Date: 13 February 2020

Please cite this article as: Lin, Z.-Y., Chen, H.-W., Lin, H.-J., Trophic model of a deep-sea ecosystem with methane seeps in the South China Sea, Deep-Sea Research Part I, https://doi.org/10.1016/ j.dsr.2020.103251. 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 Elsevier Ltd. All rights reserved.

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Trophic model of a deep-sea ecosystem with methane seeps in the

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South China Sea

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Zhe-Yu Lina, Hsuan-Wien Chenb,*, Hsing-Juh Lina,**

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a

Department of Life Sciences and Innovation and Development Center of Sustainable

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Agriculture, National Chung Hsing University, Taichung 40227, Taiwan b

Department of Biological resources, National Chiayi University, Chiayi 60004,

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Taiwan

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*Corresponding author, tel: +886-5-2717820, email: [email protected]

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**Corresponding author, tel: +886-4-22840416, email: [email protected]

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ABSTRACT

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Benthic megafauna in deep-sea ecosystems with and without methane seeps in the

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South China Sea were quantified during 2013~2016. In total, more than 190 taxa

13

were identified. Stable isotopic analyses (δ13C, δ15N) on the tissues of these

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megafauna were used to provide complementary data to reveal their trophic

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relationships. Ecopath models were constructed to show the flow of matter within

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deep-sea ecosystems. There were four integer trophic levels in both deep-sea models.

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Most of the omnivory indices of the megafauna in the models were small, indicating

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the specialized diet niches of the megafauna. The mixed trophic impact results

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showed that both models were top-down controlled. Seep-associated king crabs

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were the keystone group in the seep model. These crabs not only transferred energy

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from lower trophic levels to top predators but also linked energy from seeps to

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neighboring deep-sea ecosystems. However, the low number of trophic connections

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between the seep animals and neighboring deep-sea communities indicates the

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uniqueness of the seep ecosystems in the deep sea. All the biomass, matter flow and

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trophic transfer efficiency values were higher in the seep model than in the model

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without seeps. The higher overhead/capacity ratio in the seep model than in the

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model without seeps suggests that the former model was more resilient to

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perturbations than the latter model. Although the net primary production and

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matter flow in the seep model were lower than those in the shallow-sea models, the

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models had comparable values of biomass excluding detritus. The relatively high

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system omnivory indices and low matter cycling of both deep-sea models indicate

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that these models were more mature than the shallow-sea models. Collectively, our

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deep-sea models combined with other models suggest that deep-sea ecosystems are

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characterized by slow dynamics and high environmental stability. This study reports

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the first Ecopath model for methane seep ecosystems, which may serve as a basis for

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potential anthropogenic impact assessment and ecosystem-based management.

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Keywords: Ecopath; food web; gas hydrate; mixed trophic impacts; network analysis;

39

stable isotope analysis

40 41

Introduction In deep-sea environments, gas can be trapped in solid water to form a

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crystalline gas hydrate under high pressures and low temperatures (Sloan, 1998). Gas

43

hydrate, also known as methane hydrate because the gas is mostly methane, occurs

44

in large quantities in marine sediments below the sea floor on every continent and in

45

the permafrost in the Arctic (Kvenvolden, 1998). Methane seeps, also known as cold

46

seeps (Paull et al., 1984), are widespread in deep-sea ecosystems and result from the

47

slow leaking of gas from cracks in the seafloor in the continental margins (Sibuet and

48

Olu, 1998; Tyler et al., 2002). Recently, methane seeps have been recognized as

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potential energy sources for humankind. The demands on mass exploitation of gas

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hydrates might present large scale geological and biological hazards in the deep-sea

51

environment (Ramirez-Llodra et al., 2011). The extraction of methane would

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unavoidably disrupt surrounding habitats at the extraction site and may result in

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local biota extinction due to the highly patchy nature of seep biological communities

54

(Levin and Sibuet, 2012). With the present global climate change imposing another

55

great threat (Danovaro et al., 2017), the additive effects of human extraction

56

activities and global climate change could cause a devastating consequence to the

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deep-sea environment. However, it is poorly known the resilience of methane seep

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to anthropogenic impact (Ramirez-Llodra et al., 2011; Gollner et al, 2017), as well as

59

how this impact would rippled via the methane seep into its surrounding ecosystems

60

(Levin et al., 2016). This issue can be addressed only by increasing our understanding

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of the trophic structures and biological interactions that enable the efficient

62

functioning of organisms within the food web of methane seep ecosystems.

63

Hydrocarbons (methane) in seeps are the major energy sources of methane

64

seep ecosystems and can be oxidized by free-living or symbiotic anaerobic

65

methanotrophic archaea and sulfate-reducing bacteria through a process known as

66

the anaerobic oxidation of methane (Elvert et al., 2003; Levin, 2005). This

67

chemoautotrophic process transforms energy from hydrocarbons to bacterial

68

biomass and production, forming the food sources of methane seep ecosystems

69

(Brooks et al., 1987; Levin and Michener, 2002; Elvert et al., 2003). Methane seep

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ecosystems are often characterized by chemosynthetic bacterial mats and bacterial

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symbiotic megafauna, such as mytilid mussels, vesicomyid clams, and siboglinid

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tubeworms (Sibuet and Olu, 1998; Sibuet and Olu-Le Roy, 2002; Levin and Mendoza,

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2007). The heterogeneous habitats formed by these foundation species are inhabited

74

by a mixture of benthic fauna. The chemosynthetic nature makes methane seep

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ecosystems distinct from photosynthetic shallow-water system and become the hot

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spot of productivity and biodiversity in deep-sea environments (Sahling et al., 2003;

77

Cordes et al., 2010; Bernardino et al., 2012). Such uniqueness of the methane seep

78

ecosystem was supposed to warrant fruitful food web modeling studies; however,

79

due to the difficulty of sampling only a few ecosystem modeling studies have been

80

conducted in the deep sea > 1000 m (Smith Jr, 1992; Soetaert and van Oevelen, 2009;

81

Heymans et al., 2010). Among them, two Ecopath models conducted in the ocean

82

basin of the South China Sea (Pauly and Christensen, 1993) and in the Catalan

83

continental margin of the Mediterranean (Tecchio et al., 2013; Tecchio et al., 2015)

84

were mostly relevant. Nevertheless, there is still an urgent need for quantitative

85

models that could shed the light of potential impact on losing methane seeps in the

86

deep-sea ecosystem.

87

A bottom simulating reflector showed large quantities of gas hydrate along the

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continental slope of the northern South China Sea (Liu et al., 2006). In the past ten

89

years, more than 30 methane seeps have been discovered along the continental

90

slope (Han et al., 2014). A remotely operated vehicle (ROV) and towed camera

91

system (TowCam) were used to observe a dense population of the deep-sea

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mussel Bathymodiolus platifrons and the squat lobster Shinkaia crosnieri in the

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methane seeps of the South China Sea (Fujikura et al., 2007; Lin et al., 2007). In the

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present study, the following questions are emphasized: in what ways are the trophic

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structure and matter flow of methane seeps distinct from those of other deep-sea

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and shallow-sea ecosystems? What are the characteristics of trophic interactions

97

between methane seeps and neighboring deep-sea ecosystems? To answer these

98

questions, the first mass-balance trophic model of methane seep ecosystems was

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constructed and characterized by comparisons to other deep-sea and shallow-sea

100

ecosystems. The purposes of this study are (1) to present a model of the trophic

101

interactions within methane seep ecosystems; (2) to determine the key trophic

102

pathways and keystone species in seep ecosystems; and (3) to quantitatively

103

characterize methane seep ecosystems as a whole and by components.

104 105

Materials and methods

106

Study sites

107

Samples were collected from the methane seeps and the neighboring region

108

on the continental slope of the northern South China Sea by R/V Ocean Researcher I

109

(Taiwan), R/V Ocean Researcher III (Taiwan), R/V Ocean Researcher V (Taiwan) and

110

R/V SONNE (German) from March 2013 to September 2017. These methane seeps

111

were located in the Four-Way Closure Ridge (FWC), Formosa Ridge (FR), Pointer

112

Ridge (PR) and Horseshoe Ridge (HSR) (Fig. 1). Samples from the methane seeps

113

were collected by a TV grabber (TVG) and French-type beam trawl (opening: 0.5 m

114

high, 4 m wide; net: 9 m long; mesh size: 10 mm) to estimate the biomass of benthic

115

megafauna living in the methane seeps and neighboring deep-sea area for the

116

construction of trophic models (detailed sampling methods are described in

117

Appendix A). The sampling area, including the four seep locations, covered 1000 km2,

118

and the depth ranged from 750-1600 m. The Fiber-optical Instrumentation Towed

119

System (FITS) indicated that the methane seeps occupied an estimated 0.57% of the

120

sampling area. To characterize the trophic model with methane seeps, data collected

121

from a deep-sea area without methane seeps in PR were used to construct a control

122

model for comparison purposes.

123

Stable isotope analysis

124

Tissues of the collected megafauna were further analyzed for stable carbon and

125

nitrogen isotopes to reveal the trophic relationships of functional groups. Individual

126

specimens were treated as replicates, and only muscle tissues were used for analysis.

127

All samples were rinsed with deionized water. The tissues were freeze-dried and then

128

pulverised. Subsamples (approximately 0.5 mg) of dried tissue were enclosed in tin

129

capsules and analyzed on a continuous-flow isotope ratio mass spectrometer (Europa

130

Geo 20-20 continuous flow) coupled with an elemental analyzer (Europa ANCA) at

131

the GNS Stable Isotope Laboratory, New Zealand. Stable isotope data were reported

132

in standard notation (Appendix B) as follows:

133

δX (‰) = 1000[(Rsample/Rstandard) - 1], where R = 13C/12C or 15N/14N.

134

where δ13C or δ15N is the per-mil (‰) deviation of that sample from the

135

international standard, i.e., Pee Dee Belemnite (PDB) for the δ13C analysis and air N2

136

for the δ15N analysis (Gonfiantini et al. 1995).

137

To identify the major food sources of a given consumer, we used the Bayesian

138

Mixing Models in R (MixSIAR) (Stock and Semmens, 2016). Based on the literature

139

and our own observations, we applied trophic enrichment values of 1.0 ‰ δ13C and

140

3.4 ‰ δ15N for each trophic transfer within the food web.

141

142 143

Fig. 1. Sampling locations of the four study sites in the methane seep area on the

144

continental slope of the northern South China Sea

145

Modeling approach

146

The Ecopath routine in Ecopath with Ecosim (version 6.5.14040, Fisheries

147

Center, University of British Columbia, http://www.ecopath.org) was used to quantify

148

all organic matter flow within the food webs and construct mass-balance trophic

149

models (Christensen et al., 2005). The organic matter budget of each group (i) is

150

connected to its predator group (j) in the food web as follows:

151

×

/

=∑

×

/

× DC +

×

/

× (1 −

) + Y + E + BA (1)

152

where Pi is the production of i, Bi and Bj are the biomass of i and j, respectively, Qj is

153

the consumption of j, DCij (diet composition) is the fraction of i in the average diet of

154

j, EEi is the ecotrophic efficiency of i (i.e., the production that is passed up the trophic

155

level, exported or biomass accumulation), 1-EEi is other mortality, Yi is the fishery

156

catch of i, Ei is the export of i, and BAi is the biomass accumulation of i.

157

The consumption of a predator group (j) is then connected to its production,

158

which can be expressed as:

159

×

/

=

×

/

+

×

/

+

/

×

×

/

(2)

160

where Rj is the respiration of j, and Uj is the unused consumption of j.

161

Based on the above two equations, the B, P/B, Q/B, R/B, EE, U/Q, DC, Y, E and BA of

162

each group in the food web were required to construct the trophic model. DC had to

163

be entered, but entry was optional for one of the 4 parameters (B, P/B, Q/B, EE)

164

because Ecopath links the production of prey groups with the consumption of

165

predator groups and uses these linkages to estimate missing parameters. U/Q was

166

assumed to be 20% (Christensen et al., 2005); thus, R/B was estimated by P/B based

167

on Equation (2). Y was assumed to be zero because there was no fishery catch from

168

the deep sea. E and BA were also be assumed to be zero, as the biomass of each

169

group generally remained constant during the study period, which suggests there

170

was no obvious net migration and biomass accumulation within the food web during

171

the study period. Among these parameters, EE is the most difficult parameter to

172

determine (Christensen et al., 2005). Therefore, in this study, EE was generally

173

treated as unknown, and the Ecopath model estimated EE during the balancing

174

exercise. Biomass was expressed in tons (metric) per square kilometer (t WW km-2),

175

and matter flow was expressed in tons (metric) per square kilometer per year (t WW

176

km-2 yr-1).

177

The pedigree routine (Funtowicz and Ravetz, 1990) was used as an overall

178

index of input data quality. The pedigree index varies from 1.0 for a high-quality

179

model whose inputs are based on locally, well-sampled, high-precision data to 0.0 for

180

inputs that are taken from other models in the literature or guessed.

181

Model compartment

182

Major species of similar sizes, diets and habitats were functionally grouped

183

within the same compartment (the taxa included in each compartment are described

184

in Appendix A). Bacterial biomass was included in the compartment of organic

185

detritus, as recommended by Christensen et al. (2005), because bacterial flow may

186

totally overshadow other flows in the system. A 24-compartment model for the

187

methane seeps was developed (Table 1) and consisted of the following groups: (1)

188

large demersal fish, (2) seep-associated demersal fish, (3) seep-associated king crabs,

189

(4) other benthic crustaceans, (5) mesopelagic fish, (6) starfish, (7) seep squat

190

lobsters, (8) bathypelagic shrimps, (9) seep polychaetes, (10) mesopelagic

191

crustaceans, (11) gelatinous mesozooplankton, (12) benthic boundary layer (BBL)

192

mesozooplankton, (13) benthic suspension-feeders, (14) benthic deposit-feeders, (15)

193

seep spider crabs, (16) seep ophiuroids, (17) seep snails, (18) seep sponges, (19) seep

194

infauna, (20) seep mussels, (21) seep tubeworms, (22) seep microbial mats, (23)

195

marine snow and (24) benthic detritus. Excluding seep-associated groups, a

196

12-compartment model was developed for the control model without seeps.

197

Model balancing and verification

198

Among the input parameters, the most reliable data were the biomass values of

199

each compartment, which were obtained directly from our field data (Table 1). P/B

200

was estimated via the empirical equations with biomass, water temperature and

201

water depth input data derived from our field sampling, with the exception of

202

gelatinous mesozooplankton, BBL mesozooplankton and seep microbial mats, which

203

were derived from the relevant literature (Table 1). Half of the Q/B values were

204

estimated by referring to the P/Q values assembled from other trophic models in

205

marine ecosystems and were considered to be less reliable than biomass and P/B

206

values in our trophic models. The diet composition or DC of each group was primarily

207

derived from stable carbon and nitrogen isotope analyses (Tables 2 and 3). The DC of

208

each group was considered to be the least reliable parameter in the trophic models

209

and was gradually modified during the balancing exercise. However, most of the

210

changes were relatively small and remained within 95% of the confidence intervals of

211

input values.

212

The first step in verifying the realism of the models was to check whether the

213

EE was less than 1.0 for all living compartments, since it was assumed that the

214

consumption would not exceed the production of any living compartment. The

215

second step was to check whether the GE (the gross food conversion efficiency, i.e.,

216

the P/Q) was in the range of 0.05–0.30, as the production of most groups was

217

approximately 5–30% of the consumption. In general, the P/Q of top predators is

218

relatively low, whereas the P/Q of small-sized, growing faster taxa is relatively high. In

219

addition, the GE or P/Q cannot be higher than the net efficiency (the ratio between

220

production and assimilated food). The final step was to compare the output values to

221

relevant data from the literature on other marine models.

222

Model output and network analysis

223

Details of matter flow can be revealed by network analysis in trophic models

224

(Field et al., 1989). Mixed trophic impacts (MTI) (Ulanowicz and Puccia, 1990) were

225

calculated to assess the direct and indirect impacts of changes in the biomass of each

226

group on other groups. Keystone group in the food web were identified by

227

calculating group keystoneness by determining the overall impacts of each group on

228

other groups weighted by the proportion of the group biomass of the total biomass

229

(Libralato et al., 2006).

230

Many consumers in the trophic models were allocated to multiple discrete

231

trophic positions because they feed on several groups, which can be revealed by the

232

effective trophic level (ETL) (Odum and Heald, 1975). Lindeman trophic analysis (Kay

233

et al., 1989) was used to aggregate the complicated food web in terms of a single

234

linear food chain. The trophic efficiency of the transfer from one aggregated trophic

235

level to the next was calculated as the fraction of the energy input to a given level

236

that was transferred to the next level. For comparison with other trophic models, the

237

geometric mean of the trophic transfer efficiency from II to IV was calculated.

238

The throughput of a compartment is the total amount of matter flowing

239

through that compartment, which is a measure of compartment activity. The sum of

240

all compartment throughputs is called the total system throughput (TST). The activity

241

of each ecosystem or the TST (the sum of consumption, exports, respiratory flows,

242

and flows into detritus) was indexed in terms of how much matter the system

243

processed. The cycling of matter is considered a critical process in ecosystem

244

functioning because it can facilitate homeostatic control over the magnitude of flows

245

(Odum, 1969). The Finn cycling index (FCI) of the cycle analysis (Kay et al., 1989), i.e.,

246

the relative importance of cycling to the total flow, was used to measure how

247

retentive the ecosystem was.

248

The connectance index (CI) was used to measure the observed number of links

249

within the food web relative to the number of possible links (Odum, 1969). The

250

system omnivory index (SOI), a measure of the distribution of feeding interactions

251

among trophic levels, was used for evaluating the complexity and connectivity within

252

the food web (Christensen et al., 2005).

253

Information theory was further used to characterize the structural properties

254

of the food web (Ulanowicz, 2001). Ascendency (A) can be regarded as the organized

255

power, which is a measure of the magnitude of the matter flowing through a food

256

web. A was calculated by multiplying TST with the average mutual information

257

among compartments. During the development of an ecosystem, the upper limit of A

258

is the development capacity (C), which is the potential development of that

259

ecosystem (Kay et al., 1989). The difference between C and A is the overhead (O),

260

which offers the ecosystem potential repertoires that can adopt to survive under

261

new circumstances. Without sufficient overhead, a system is unable to create

262

effective responses to the challenges presented by its environment (Rutledge et al.,

263

1976).

264 265

Results

266

Model balancing and verification

267

The pedigree indices of both models were 0.49 and 0.50. Some of the initial

268

outputs of EE were >1.0, so the DCs of some compartments were adjusted to lower

269

the predation pressure. The prey biomasses of large demersal fish, seep-associated

270

demersal fish, seep-associated king crabs, and mesopelagic fish were found to be

271

insufficient to meet their needs. Parts of the DCs of these groups were thus set as

272

imports from outside of the model area because these predators have high mobility

273

for feeding.

274

The inputs and outputs of the trophic models are listed in Tables 4 and 5. After

275

some of the DCs were adjusted, all the EEs were <1.0. Most of the P/Qs ranged from

276

0.05–0.30. The P/Qs of gelatinous mesozooplankton, BBL mesozooplankton, benthic

277

deposit-feeders, seep ophiuroids, and seep snails were >0.30, as these species are

278

small-sized and grow relatively fast. All respiration (R) values were positive, and the

279

net efficiency (NE) values were higher than the P/Q values. These outputs indicated

280

that the two trophic models were realistic.

281

Table 1 Data sources of input parameters for each compartment of the deep-sea models Group name

282

B

P/B

Q/B

P/Q

D/C

1. large demersal fish

this study

Fishbase

Fishbase

-

this study

2. seep-associated demersal fish

this study

Fishbase

Fishbase

-

this study

3. mesopelagic fish

this study

Fishbase

Fishbase

-

this study

4. seep-associated king crabs

this study

Brey (2012)

Falk-Petersen (2004)

-

this study

5. bathypelagic shrimps

this study

Brey (2012)

-

Falk-Petersen (2004)

Tecchio et al. (2013)

6. starfish

this study

Brey (2012)

Aydin et al. (2002)

-

this study this study

7. other benthic crustaceans

this study

Brey (2012)

-

Tecchio et al. (2013)

8. seep squat lobsters

this study

Brey (2012)

-

Neira and Arancibia (2007) this study

9. seep spider crabs

estimated by Ecopath

Brey (2012)

Falk-Petersen (2004)

-

10. seep ophiuroids

this study

Brey (2012)

Criales-Hernandez et al. (2006) -

this study

11. mesopelagic crustaceans

this study

Brey (2012)

-

Tecchio et al. (2013)

Falk-Petersen (2004)

Feng et al. (2015)

12. seep snails

this study

Brey (2012)

Falk-Petersen (2004)

-

this study

13. gelatinous mesozooplankton

this study

Tecchio et al. (2013)

Tecchio et al. (2013)

-

Tecchio et al. (2013)

14. BBL mesozooplankton

Tecchio et al. (2013)

Tecchio et al. (2013)

Tecchio et al. (2013)

-

Tecchio et al. (2013)

15. seep polychaetes

this study

Brey (2012)

-

Falk-Petersen (2004)

Britayev et al. (2003)

16. benthic suspension-feeders

this study

Brey (2012)

-

Blanchard et al. (2002)

Tecchio et al. (2013)

17. benthic deposit-feeders

this study

Brey (2012)

Falk-Petersen (2004)

-

Tecchio et al. (2013)

18. seep sponges

estimated by Ecopath

Brey (2012)

-

Blanchard et al. (2002)

Feng et al. (2015)

19. seep infauna

estimated by Ecopath

Duarte and García (2004)

Duarte and García (2004)

-

Levin and Michener (2002)

20. seep tubeworms

this study

Brey (2012)

-

-

-

21. seep mussels

this study

Brey (2012)

-

-

-

22. seep microbial mats

Lichtschlag et al. (2010)

Lichtschlag et al. (2010)

-

-

-

23. marine snow

Hsu (2010)

-

-

-

-

24. benthic detritus

this study

-

-

-

-

B, biomass; P/B, production/biomass ratio; Q/B, consumption/biomass ratio; P/Q, production/consumption ratio; DC, diet composition

283

Table 2. Diet compositions for each compartment of the methane seep model after balancing. Diets for each compartment were summed to 1. Prey \ predator

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

1

large demersal fish

0.006

0.114

0.000

0.047

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2

seep-associated demersal fish

0.002

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

3

mesopelagic fish

0.001

0.001

0.001

0.001

0.000

0.000

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

4

seep-associated king crabs

0.031

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

5

bathypelagic shrimps

0.128

0.059

0.005

0.035

0.000

0.000

0.085

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

6

starfish

0.012

0.013

0.001

0.011

0.000

0.011

0.010

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

7

other benthic crustaceans

0.166

0.231

0.000

0.114

0.000

0.108

0.050

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

8

seep squat lobsters

0.000

0.065

0.000

0.039

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

9

seep spider crabs

0.000

0.064

0.000

0.070

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

10 seep ophiuroids

0.000

0.065

0.000

0.035

0.000

0.000

0.000

0.190

0.045

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

11 mesopelagic crustaceans

0.000

0.000

0.060

0.000

0.010

0.000

0.000

0.000

0.000

0.000

0.100

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

12 seep snails

0.000

0.067

0.000

0.070

0.000

0.000

0.000

0.052

0.097

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

13 gelatinous mesozooplankton

0.000

0.000

0.587

0.000

0.095

0.000

0.000

0.000

0.000

0.000

0.030

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

14 BBL mesozooplankton

0.117

0.000

0.000

0.000

0.410

0.507

0.449

0.000

0.000

0.000

0.180

0.000

0.200

0.050

0.000

0.000

0.000

0.000

0.000

15 seep polychaetes

0.000

0.070

0.000

0.042

0.000

0.000

0.000

0.064

0.048

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

16 benthic suspension-feeders

0.010

0.010

0.001

0.052

0.000

0.005

0.010

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

17 benthic deposit-feeders

0.103

0.106

0.001

0.191

0.010

0.081

0.050

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

18 seep sponges

0.000

0.000

0.000

0.057

0.000

0.000

0.000

0.170

0.058

0.390

0.000

0.268

0.000

0.000

0.000

0.000

0.000

0.000

0.000

19 seep infauna

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.136

0.412

0.115

0.000

0.090

0.000

0.000

0.000

0.000

0.000

0.000

0.000

20 seep tubeworms

0.000

0.063

0.000

0.069

0.000

0.000

0.000

0.086

0.102

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.041

0.070

21 seep mussels

0.000

0.071

0.000

0.064

0.000

0.000

0.000

0.075

0.061

0.000

0.000

0.000

0.000

0.000

0.768

0.000

0.000

0.100

0.000

22 seep microbial mats

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.100

0.000

0.379

0.000

0.430

0.000

0.000

0.100

0.000

0.000

0.789

0.558

23 marine snow

0.000

0.000

0.000

0.000

0.100

0.000

0.000

0.069

0.000

0.063

0.230

0.098

0.450

0.950

0.099

0.850

0.100

0.035

0.229

24 benthic detritus

0.000

0.000

0.000

0.103

0.000

0.288

0.345

0.059

0.177

0.053

0.000

0.115

0.000

0.000

0.033

0.150

0.900

0.035

0.143

0.423

0.000

0.343

0.000

0.375

0.000

0.000

0.000

0.000

0.000

0.460

0.000

0.350

0.000

0.000

0.000

0.000

0.000

0.000

Import

284 285

Table 3. Diet compositions for each compartment of the deep-sea model without seeps after balancing. Diets for each compartment were summed to 1. Prey \ predator

1

2

3

5

1 large demersal fish

0.005

2 mesopelagic fish

0.001

0.005

0.001

3 bathypelagic shrimps

0.100

0.005

0.085

4 starfish

0.010

5 other benthic crustaceans

0.352

0.001

0.010

0.005

0.200

0.050

6

7

8

0.180

0.200

0.050

0.230

0.450

0.950

0.460

0.350

6 mesopelagic crustaceans

0.060

0.010

0.100

7 gelatinous mesozooplankton

0.585

0.095

0.030

8 BBL mesozooplankton

0.105

9 benthic suspension-feeders

0.001

0.001

0.050

0.001

10 benthic deposit-feeders

0.410

11 marine snow

0.010

Import

0.473

0.480

0.001

0.004

0.050

0.030

0.100

12 benthic detritus

286

4

0.266 0.376

0.342

0.375

0.345

9

10

0.850

0.100

0.150

0.900

287

Table 4. Input and output (in bold) parameters for each compartment of the seep model Group name 1 large demersal fish

B

P/B

Q/B

EE

P/Q

NE

R

U/Q

OI

FD

RTI

0.096

0.31

5.83

0.67

0.05

0.07

0.42

0.20

0.78

0.12

1.00

2 seep-associated demersal fish

3.45

0.006

0.27

4.27

0.83

0.06

0.08

0.02

0.20

0.51

0.01

0.19

3 mesopelagic fish

3.34

0.002

1.09

10.97

0.99

0.10

0.12

0.01

0.20

0.43

0.00

0.02

4 seep-associated king crabs

3.09

0.024

0.81

12.06

0.91

0.07

0.08

0.21

0.20

0.53

0.06

0.89

5 bathypelagic shrimps

2.93

0.035

4.17

27.77

0.97

0.15

0.19

0.62

0.20

0.44

0.20

0.23

6 starfish

2.84

0.017

1.14

5.00

0.93

0.23

0.28

0.05

0.20

0.34

0.02

0.01

7 other benthic crustaceans

2.81

0.039

4.52

17.01

0.99

0.27

0.33

0.35

0.20

0.42

0.13

0.15

8 seep squat lobsters

2.73

1.874

1.31

4.67

0.01

0.28

0.35

4.55

0.20

0.37

4.19

0.33

9 seep spider crabs

2.72

0.028

0.81

12.06

0.95

0.07

0.08

0.25

0.20

0.28

0.07

0.07

10 seep ophiuroids

2.51

1.066

1.73

3.00

0.92

0.58

0.72

0.72

0.20

0.25

0.79

0.09

11 mesopelagic crustaceans

2.49

0.009

9.69

64.61

0.78

0.15

0.19

0.39

0.20

0.54

0.14

0.02

12 seep snails

2.36

0.530

1.01

2.22

0.95

0.45

0.57

0.41

0.20

0.23

0.26

0.08

13 gelatinous mesozooplankton

2.32

0.287

22.00

56.00

0.02

0.39

0.65

3.32

0.40

0.29

12.61

0.20

14 BBL mesozooplankton

2.05

0.282

18.00

50.00

0.95

0.36

0.60

3.38

0.40

0.05

5.89

0.15

15 seep polychaetes

2.00

0.277

2.76

18.43

0.77

0.15

0.19

3.32

0.20

0.00

1.20

0.10

16 benthic suspension-feeders

2.00

0.067

0.60

2.19

0.69

0.27

0.34

0.08

0.20

-

0.04

0.04

17 benthic deposit-feeders

2.00

0.141

1.17

2.22

0.99

0.53

0.66

0.09

0.20

-

0.06

0.10

18 seep sponges

2.00

5.410

0.60

2.19

0.95

0.27

0.34

6.24

0.20

-

2.54

0.11

19 seep infauna

2.00

0.271

7.00

27.00

0.95

0.26

0.40

2.86

0.35

-

2.66

0.14

20 seep tubeworms

1.00

39.891

1.34

-

0.03

-

-

-

-

-

51.50

0.07

21 seep mussels

1.00

44.738

0.59

-

0.22

-

-

-

-

-

20.75

0.14

22 seep microbial mats

1.00

3.800

59.05

-

0.07

-

-

-

-

-

207.85

0.14

23 marine snow

1.00

52.280

-

-

0.47

-

-

-

-

-

-

-

1.00

69.005

-

-

0.01

-

-

-

-

-

-

-

24 benthic detritus

288 289

TL 3.54

-2

-1

-1

TL: trophic level, B: biomass (t km ), P/B: production/biomass (yr ), Q/B: consumption/biomass (yr ), EE: ecotrophic efficiency, P/Q: production/consumption, NE: net efficiency, R: respiration (t km-2 yr-1), U/Q: unassimilated/consumption, OI: omnivory index, FD: flow to

290

detritus (t km-2 yr-1), RTI: relative total impact

291

Table 5. Input and output (in bold) parameters for each compartment of the deep-sea model without seeps Group name 1 large demersal fish

TL

B

P/B

Q/B

EE

P/Q

NE

R

U/Q

OI

FD

RTI

3.63

0.021

0.24

3.47

0.07

0.07

0.08

0.05

0.20

0.70

0.02

0.26

2 mesopelagic fish

3.34

0.001

0.59

10.19

0.63

0.06

0.07

0.01

0.20

0.43

0.00

0.15

3 bathypelagic shrimps

2.93

0.008

3.84

25.61

0.97

0.15

0.19

0.13

0.20

0.44

0.04

0.61

4 starfish

2.93

0.002

1.40

5.00

0.86

0.28

0.35

0.00

0.20

0.40

0.00

0.04

5 other benthic crustaceans

2.81

0.017

3.88

14.57

0.61

0.27

0.33

0.13

0.20

0.42

0.07

1.00

6 mesopelagic crustaceans

2.49

0.004

5.01

33.37

0.79

0.15

0.19

0.09

0.20

0.54

0.03

0.08

7 gelatinous mesozooplankton

2.32

0.066

22.00

56.00

0.02

0.39

0.65

0.76

0.40

0.29

2.89

0.49

8 BBL mesozooplankton

2.05

0.369

18.00

50.00

0.28

0.36

0.60

4.43

0.40

0.05

12.13

0.51

9 benthic suspension-feeders

2.00

0.002

0.58

2.10

0.93

0.27

0.34

0.00

0.20

-

0.00

0.01

10 benthic deposit-feeders

2.00

0.015

1.15

2.22

0.80

0.52

0.65

0.01

0.20

-

0.01

0.05

11 marine snow

1.00

52.280

-

-

0.37

-

-

-

-

-

-

-

1.00

66.877

-

-

0.01

-

-

-

-

-

-

-

12 benthic detritus

-2

-1

-1

292 293

TL: trophic level, B: biomass (t km ), P/B: production/biomass (yr ), Q/B: consumption/biomass (yr ), EE: ecotrophic efficiency, P/Q: production/consumption, NE: net efficiency, R: respiration (t km-2 yr-1), U/Q: unassimilated/consumption, OI: omnivory index, FD: flow to

294

detritus (t km-2 yr-1), RTI: relative total impact

295

Table 6. Comparison of ecosystem attributes of the model with seeps and without seeps Attribute Sum of all consumption

Without seep

Units

71.57

22.83

t km yr

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

Sum of all exports

335.75

48.12

t km yr

Sum of all respiratory flows

27.30

5.61

t km yr

Sum of all flows into detritus

363.37

67.48

t km yr

Total system throughput (TST)

797.99

144.05

t km yr

Sum of all production

327.10

8.23

t km yr

Calculated total net production (NP)

304.27

0.00

t km yr

Total net production/total respiration (P/R)

11.15

0.00

Net system production (NSP)

276.97

-5.61

t km yr

Total net production/Total biomass (P/B)

3.08

0.00

Total biomass/total throughput (B/T)

0.12

0.003

yr

Total biomass (excluding detritus)

98.89

0.50

t km

-2

Total biomass of consumers

10.46

0.50

t km

-2

Connectance Index (CI)

0.25

0.46

System Omnivory Index (SOI)

0.24

0.31

Finn's cycling index (FCI)

0.26

0.69

-1

% of TST

Finn's mean path length

2.20

2.68

Ascendency/capacity (A/C)

48.86

54.65

% of capacity

51.14

45.35

% of capacity

2495.00

383.30

flowbits

Overhead/capacity (O/C) Capacity

296

With Seep

Detritivory/production (D/P) Transfer efficiencies

1.14

-

6.35

4.08

Shannon diversity index

1.26

1.00

%

297

Table 7. Comparison of ecosystem attributes between the model with seeps and the model without seeps and other marine trophic models Shallow-water

Deep-sea South China Sea

Water depth Sum of all consumption

Mediterranean Sea

Eastern Taiwan

East China Sea

Units

Adriatic Sea

Western Taiwan

50-400

≈75

10-50

≈100

≈200

m

852.11

1305.04

453.00

824.88

1289.25

t km yr

With seep

Without seep

Catalan continental slope

Southern Catalan Sea

750-1900

1100-1900

1000-1400

71.57

22.83

51.36

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

-2

-1

Sum of all exports

335.75

48.12

20.09

61.27

730.15

564.00

810.00

2518.65

t km yr

Sum of all respiratory flows

27.30

5.61

20.19

327.16

421.09

268.00

528.62

715.95

t km yr

Sum of all flows into detritus

363.37

67.48

65.84

416.91

1387.46

690.00

890.94

2686.87

t km yr

Total system throughput (TST)

797.99

144.05

157.48

1657.00

3844.00

1975.00

3054.43

7221.00

t km yr

Sum of all production

327.10

8.23

14.83

658.00

1566.00

925.00

1787.29

3549.00

t km yr

Calculated total net primary production (NPP)

304.27

0.00

0.00

386.68

1149.85

832.00

1656.00

3234.60

t km yr

Total primary production/total respiration (P/R)

11.15

0.00

0.00

1.18

2.73

3.10

3.13

4.52

-

Net system production (NSP)

276.97

-5.61

-20.19

59.52

728.76

564.00

1127.38

2518.65

t km yr

Total primary production/Total biomass (P/B)

3.08

0.00

0.00

6.55

8.82

71.20

85.77

91.05

yr

Total biomass/total throughput (B/T)

0.12

0.00

0.02

0.04

0.03

0.01

0.01

0.01

t km

Total biomass (excluding detritus)

98.89

0.50

3.93

58.99

130.30

11.7

19.30

35.53

t km

Connectance Index (CI)

0.25

0.46

-

0.20

-

-

0.47

-

-

-2

-1

-1 -2 -2

System Omnivory Index (SOI)

0.24

0.31

0.29

0.19

0.19

-

0.25

-

-

Finn's cycling index (FCI)

0.26

0.69

4.20

25.19

14.70

2.50

-

-

% of TST

Detritivory: Herbivory ratio (D/H)

1.14

-

-

1.12

1.68

0.50

0.12

0.18

-

Transfer efficiencies (TE)

6.35

4.08

15.70

12.60

10.00

11.2

19.60

16.30

%

Ascendency:capacity ratio (A/C)

48.86

54.65

-

25.50

27.00

-

-

-

% of capacity

51.14

45.35

-

74.50

73.00

-

-

-

% of capacity

2495.00

383.30

-

7119.30

15406.70

-

-

-

flowbits

0.67

0.66

0.52

-

-

Overhead:capacity ratio (O/C) Capacity Ecopath pedigree index Reference

0.49

0.50

0.54

this study

this study

Tecchio et al., 2013

Coll et al., 2006 Coll et al., 2007

0.73 Hsiao, 2006

Tsai, 2014 Cheng et al., 2009

-

298 299

Trophic structure and matter flow There were approximately four integer trophic levels in the seep model (Fig. 2a).

300

Large demersal fish were the top predators (3.54), followed by seep-associated

301

demersal fish (3.45), mesopelagic fish (3.34) and seep-associated king crabs (3.09).

302

The first trophic level consisted of chemosynthetic mussels, tubeworms and

303

microbial mats, marine snow, and benthic detritus. In the deep-sea model without

304

seeps (Fig. 2b), the trophic structure was similar to that in the seep model, excluding

305

the seep-associated groups. Large demersal fish were also the top predators (3.63).

306

There were no chemosynthetic mussels, tubeworms and microbial mats in the

307

deep-sea model without seeps. Most of the omnivory indices in the two models were

308

small, with a range of 0.05–0.54, indicating their diet niches were generally

309

specialized (Tables 4 and 5).

a

310 b

311 312 313 314 315

Fig. 2. Trophic model of (a) the methane seeps and (b) the deep-sea area without seeps. Circle size is proportional to the log of the compartmental biomass; lines indicate feeding relationships, and line width indicates the proportion of the diet composition.

316

In the trophic model of the methane seeps, the EE values of seep mussels and

317

marine snow were 0.22 and 0.47, respectively, whereas the EE values of seep

318

tubeworms, seep microbial mats, and benthic detritus were only 0.03, 0.07, and 0.01,

319

respectively, indicating seep mussels were the main food sources for the seep

320

ecosystem. Most of the EE values of the consumers in the two models ranged from

321

0.61 to 0.99, indicating high consumption by predators occurred in the deep-sea

322

ecosystems. The EE values of large demersal fish, seep squat lobsters, and gelatinous

323

mesozooplankton were relatively low (0.01-0.67), suggesting little predation on these

324

organisms occurred in the deep-sea environment (Tables 4 and 5).

325

In the seep model, the Lindeman spine showed that the matter flow from the

326

primary producers (i.e., seep mussels, seep tubeworms and seep microbial mats) to

327

trophic level II was approximately the same as the flow from organic detritus to

328

trophic level II, although the biomass of the primary producers was 73% of the mass

329

of detritus (Fig. 3a). The trophic transfer efficiency decreased from 19.9% at trophic

330

level II to 1.3% at trophic level IV, with a geometric average of 6.35%. In the deep-sea

331

model without seeps, detritus was the only food source (Fig. 3b). All the biomass and

332

matter flow values in the Lindeman spine were much lower than those in the seep

333

model. The trophic transfer efficiencies were thus lower at all trophic levels (2-5%)

334

with a geometric average of 4.08%.

a

b

335 336 337 338 339 340 341 342 343 344 345

Fig. 3. Lindeman trophic analysis of (a) the seep model and (b) the model without seeps. The compartmental throughputs (t m-2 yr-1) of the compartments were aggregated into a concatenated chain of transfers through 4 integer trophic levels. Flows from primary producers (P) and from detritus (D) and flows out of the bottoms represent respiration. The trophic efficiencies (%) of transfers from 1 aggregated trophic level to the next were calculated as the fraction of the input of organic matter to a given level that was transferred to the next level. Trophic level I: producers; trophic level II: herbivores or primary consumers; trophic level III: carnivores or secondary consumers; trophic level IV: top carnivores or tertiary consumers.

346

Mixed trophic impact and keystoneness

347

The mixed trophic impact results showed that the seep model was obviously

348

top-down controlled. The three top predators, large demersal fish, seep-associated

349

king crabs, and seep squat lobsters, generally had highly negative impacts on other

350

groups (Fig. 4a). However, seep-associated king crabs had an indirectly positive

351

impact on seep ophiuroids, seep snails and seep polychaetes, as seep-associated king

352

crabs predate on seep squat lobsters, which are predators of these three groups.

353

Benthic detritus had a positive impact on benthic deposit-feeders, starfish, and other

354

benthic crustaceans that feed directly on benthic detritus. Marine snow also had a

355

positive impact on benthic suspension feeders, gelatinous mesozooplankton, BBL

356

mesozooplankton, and bathypelagic shrimps that feed on suspension detritus.

357

However, these positive impacts were relatively small when compared to the

358

negative impacts posed by the predators in the food web. The model without seeps

359

was also obviously top-down controlled, as the most influential group in the trophic

360

model was other benthic crustaceans, which had a highly negative impact on many

361

other groups (Fig. 4b).

362

The keystoneness analysis showed that in the seep model, large demersal fish were

363

ranked as the first keystone group (1.00), followed by seep-associated king crabs

364

(0.89) and seep squat lobsters (0.33) (Fig. 5a). However, the keystone group differed

365

in the model without seeps. Other benthic crustaceans was the only keystone group

366

in the model without seeps (Fig. 5b).

a

367 368 b

369 370 371 372 373 374 375

Fig. 4 Mixed trophic impacts of (a) the seep model and (b) the model without seeps, showing direct and indirect impacts that an increase in the biomass of one compartment (histogram row) would have on another compartment (histogram column). White (black) bars pointing upwards (downwards) indicate positive (negative) impacts. Impacts are relative, not absolute but are comparable between histograms.

a

376 b

377 378 379 380 381

Fig. 5. Total ecosystem impact (ε) vs. keystoneness for (a) the seep model and (b) the model without seeps. Groups with high values of total impact and keystoneness impose a large influence on the ecosystem. The numbers refer to the group numbers in Tables 1 and 2.

382 383

Ecosystem attributes The TST of the seep model was 5.5 times that of the model without seeps in

384

terms of the flow per unit area (Table 6). As there was no net primary production in

385

the model without seeps, the P/R and P/B were zero. The total biomass (excluding

386

detritus) and total biomass of consumers in the seep model were 200 and 21 times

387

higher, respectively, that those in the model without seeps. Consequently, the B/T of

388

the seep model was much higher than that of the model without seeps.

389

The CI and SOI showed that the model without seeps was slightly more

390

complex than the seep model in terms of connectivity within the food web.

391

Therefore, Finn's mean path length showed that organic matter stayed longer in the

392

model without seeps than in the seep model. Although there was primary

393

production in the seep model, the D/P (1.14) showed that the matter source from

394

detritus was slightly higher than that from primary production. However, the Finn’s

395

cycling index for both models was low (<0.7% TST). The transfer efficiencies for both

396

models were also low (<10%). The transfer efficiency in the seep model was higher

397

than that in the model without seeps.

398

The model without seeps appeared to be more stable than the model with

399

seep because the A/C was higher in the former than in the latter. However, the O/C in

400

the seep model was higher than that in the model without seeps, which suggests

401

that the seep model was more resilient than the model without seeps to

402

perturbations.

403 404 405

Discussion The trophic models with methane seeps and without seeps showed similar

406

trophic structures. Both models comprised four integer trophic levels. With the

407

exception of large demersal fish, both models were characterized by small omnivory

408

indices (<0.60) for all the groups, suggesting that the deep-sea organisms had less

409

cross trophc levels feeding, regardless of whether seeps were present. This finding is

410

consistent with the findings of the Catalan deep-sea model (Tecchio et al., 2013).

411

However, it is unclear that whether the narrow diet niches across trophic levels

412

observed in this study imply specialized food habits within same trophic level for

413

these deep-sea species or not .

414

Because there was chemosynthetic primary production at the methane seeps,

415

the matter flow and biomass values in the seep model were higher than those in the

416

model without seeps. Consequently, the trophic transfer efficiencies were higher in

417

the seep model than in the model without seeps. In addition, the energy produced

418

by chemosynthesis in methane seeps can provide the ecosystem potential reservoirs

419

with high O/C that can result in effective responses to the challenges presented by

420

the environment. However, the geometric mean transfer efficiencies in both models

421

were much lower than the global geometric mean transfer efficiency of aquatic

422

ecosystems (9.2%) (Christensen and Pauly, 1993).

423

The keystoneness analysis showed that seep-associated king crabs were the

424

keystone group in the seep model. Prior studies also recorded the occurrence of king

425

crabs in methane seeps (Macpherson, 1994; Barry et al., 1996; Martin and Haney,

426

2005; Wang et al., 2016). Our δ13C and δ15N analysis also showed that the

427

seep-associated king crabs might derive mixed carbon and nitrogen sources from the

428

methane seep and photosynthesis system (δ13C=-29.5‰；δ15N=8.6‰, see Appendix B).

429

This result indicated that the seep-associated king crabs not only transferred energy

430

from lower trophic levels to top predators but also linked the energy from methane

431

seeps to neighboring deep-sea ecosystems. Seep-associated king crabs can thus be

432

considered a biological indicator, enabling the functioning or matter flow of

433

methane-seep ecosystems in the deep sea to be monitored under human

434

perturbations and climate change (Danovaro et al., 2017).

435

The negative net system production (NSP) of the model without seeps

436

indicated that the ecosystem depended more upon external inputs of organic matter

437

produced by photosynthesis in distant shallow waters or by chemosynthesis in

438

methane seeps. The Finn’s cycling index of both models showed that matter cycling

439

within the food web was rather low (<0.7%), regardless of whether there were seeps.

440

This indicated that the contribution of cycling to the matter flow of both models was

441

minor, which is consistent with the low Finn’s cycling index of the Catalan deep-sea

442

model (Tecchio et al., 2013). Low cycling can also be observed in the upwelling model

443

of Tongoy Bay (Ortiz and Wolff, 2002), where the ecosystem depends greatly upon

444

the external input of organic matter via upwelling. Nevertheless, the high cycling of

445

organic matter via the consumption of prey by top predators may indicate that an

446

ecosystem is responding to a disturbance (Pranovi and Link, 2009), which was

447

obviously not the case in our two deep-sea models.

448

Although there were seep-associated animals in the seep model, the low

449

connectance and system omnivory index of the seep model were unexpected and

450

suggested that the trophic structure of the seep model was less complex than that of

451

the model without seeps. This result can be attributed to the low number of trophic

452

connections among seep-associated groups and their links to neighboring deep-sea

453

communities, indicating the rather isolated and unique nature of the methane seep

454

food web in the deep-sea environments (Danovaro et al., 2017). Further comparative

455

study to distinguish network properties of methane seep food webs from their

456

neighboring non-seep ones should be remarkable.

457

To characterize the deep-sea models in the South China Sea, the two deep-sea

458

models were compared with other deep-sea and shallow-sea models (Table 7).

459

Similar to our two models of the continental slope of the South China Sea, the

460

Catalan deep-sea model was in a continental slope region (Tecchio et al., 2013). All

461

three ecosystems were in the bathyal zone with a depth of 1000-4000 m. The

462

shallower Southern Catalan Sea model (Coll et al., 2006) was located in the

463

continental shelf and upper continental slope regions (50-400 m deep). The Adriatic

464

Sea model (Coll et al., 2007) was located in the continental shelf region with a mean

465

depth of 75 m. The eastern Taiwan model (Tsai, 2014), which was influenced by the

466

Kuroshio Current, was located at the edge of the continental shelf (100 m). The East

467

China Sea model (Cheng et al., 2009), which was influenced by the Yangtze River, was

468

also located in the continental shelf region (200 m). The Western Taiwan model

469

(Hsiao, 2006) was located in the continental shelf region of the Taiwan Strait (10-50

470

m). These shallow-sea models were generally in the epipelagic zone (<200 m deep).

471

The food sources of our South China Sea model without seeps and the Catalan

472

continental slope model were marine snow and benthic detritus, as there was no

473

chemosynthetic production. The ecosystem attributes of these deep-sea models

474

compared with other shallow-sea models showed lower TST and production for the

475

deep-sea ecosystems (Table 7). Despite this, the proportion of TST flowing into the

476

detrital pool was higher in the deep-sea models (40–45%) than in the shallow-sea

477

models (25–35%), indicating that a greater proportion of matter was unused by

478

consumers or not exported outside in the deep-sea ecosystems than in the

479

shallow-sea ecosystems. Nevertheless, this negative net system production also

480

suggests that the studied deep-sea ecosystems were supported by the external input

481

of organic matter from outside systems. The omnivory indices of these systems were

482

higher than those of shallow-sea models, suggesting that the trophic relationships of

483

these deep-sea models were relatively complex. However, the Finn’s cycling indices

484

of the three deep-sea models were lower than those of the shallow-sea models,

485

suggesting that the cycling was lower in the former than in the latter and that the

486

disturbances faced by the deep-sea ecosystems was relatively minor compared to

487

that faced by the shallow-sea ecosystems. The D:H ratio of our seep model was >1,

488

suggesting that the food source of the deep-sea ecosystem was most reliant on

489

detritus than on primary production, which is consistent with the D:H ratios >1 of the

490

shallow-sea ecosystems in the Mediterranean Sea. However, the D:H ratios were <1

491

in the shallow-sea ecosystems in the East Pacific, indicating herbivory was the

492

dominant food source contributing to matter flow. The inconsistence suggests that

493

the D:H ratio of an ecosystem is affected more by the local environmental conditions

494

than by seawater depth.

495

Although the TST, sum of all production, and total net primary production

496

values in our seep model were lower than those in the shallow-sea models,

497

depending upon the input of photosynthetic energy, the biomass excluding detritus

498

in the seep model was comparable to the biomass in the shallow-sea models (Table

499

7). Previous studies have reported a rich abundance of infauna depending upon the

500

methane-derived carbon sources in methane seeps, with up to 7000-9000 individuals

501

m-2 (Bernardino and Smith, 2010; Grupe et al., 2015). In addition, the deep-sea

502

models in the South China Sea and the Catalan continental slope were relatively

503

more mature than the shallow-sea models. The P/B and B/T values of the deep-sea

504

models were obviously lower and higher, respectively, than those of the shallow-sea

505

ecosystems. The system omnivory indices and A/C ratios were generally higher in the

506

deep-sea models than in the shallow-sea models, indicating that the deep-sea

507

trophic structures were more stable than the shallow-sea ecosystems. Conversely,

508

the proportion of matter cycling and the transfer efficiencies in the deep-sea models

509

were generally lower than those in the shallow-sea models. Collectively, our

510

deep-sea models combined with the Catalan continental slope model suggest that

511

deep-sea ecosystems are characterized by slow dynamics and high environmental

512

stability, as indicated by Montserrat et al. (2019).

513

Conclusions

514

The results of both models should be viewed as a preliminary approximation of

515

the interactions occurring in deep-sea ecosystems. It should be stressed that some

516

input values are rough estimates only. The major limitation of this study is that the

517

sampling device was unable to effectively quantify the biomasses of macrobenthos

518

and meiobenthos and their diet composition. This might underestimate the matter

519

transfer from trophic levels I to II in the trophic model. However, the pedigree indices

520

of both models (0.49 and 0.50) are comparable to those of other marine models.

521

Based on the comparison of ecosystem attributes with other marine models, the

522

major pathways of the deep-sea system are accurately depicted in the models, and

523

an integrated picture of the seep model was obtained. This study reports the first

524

methane seep model and thus may serve as a basis for future comparisons and for

525

ecosystem management. Further work is required to improve the input parameters

526

and verify the results of this pilot model.

527

Acknowledgements

528

We are grateful for the support of the National Energy Program-Phase II (NEP II)

529

under grant no. 104-3113-M-005-001, 105-3113-M-005-001 and

530

106-3113-M-005-001 from the Ministry of Science and Technology (MOST) of Taiwan.

531

This work was financially supported in part by the “Innovation and Development

532

Center of Sustainable Agriculture” from The Featured Areas Research Center Program

533

within the Higher Education Sprout Project by the Ministry of Education (MOE) of

534

Taiwan. We also thank K. S. Lee (NMNS), T. Y. Chan, T. W. Wang (NTOU), L. L. Liu, C. C.

535

Wang, H. H. Chen (NSYSU), M. C. Lai, S. C. Chen, C. L. Lee, C. C. Lu (NCHU) and crew

536

members of OR1 for their helps during project.

537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561

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Highlights Ecopath models were constructed to show matter flow within deep-sea ecosystems. Deep-sea models were more mature than shallow-sea models. King crabs were keystone group in the deep-sea models with methane seeps. All biomass, matter flow and transfer efficiency were higher in the seep model. The seep model was more resilient to perturbations than the model without seeps.

Declaration of Interest Statement The authors declare that they have no conflict of interest.