Accepted Manuscript Impacts of sea cucumber farming on biogeochemical characteristics in the Yellow River estuary, Northern China Jing Fu, Hisashi Yokoyama, Baoshan Cui, Jin Zhou, Jiaguo Yan, Xu Ma, Shozo Shibata PII:
S1474-7065(16)30261-3
DOI:
10.1016/j.pce.2016.12.006
Reference:
JPCE 2543
To appear in:
Physics and Chemistry of the Earth
Received Date: 20 October 2016 Revised Date:
8 December 2016
Accepted Date: 16 December 2016
Please cite this article as: Fu, J., Yokoyama, H., Cui, B., Zhou, J., Yan, J., Ma, X., Shibata, S., Impacts of sea cucumber farming on biogeochemical characteristics in the Yellow River estuary, Northern China, Physics and Chemistry of the Earth (2017), doi: 10.1016/j.pce.2016.12.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
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Impacts of sea cucumber farming on biogeochemical characteristics in the Yellow River
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estuary, Northern China
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Jing Fu1 , Hisashi Yokoyama2, Baoshan Cui3, Jin Zhou4, Jiaguo Yan3, Xu Ma3, Shozo
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Shibata1
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Japan 2
Field Science Education and Research Center, Kyoto University, Kyoto 6068502, Japan
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State Key Laboratory of Water Environment Simulation, School of Environment, Beijing
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Normal University, Beijing 100875, P.R. China 4
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East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences,
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Graduate School of Global Environmental Studies, Kyoto University, Kyoto 6068501,
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Present address. Graduate School of Global Environmental Studies, Kyoto University,
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Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan. Tel.: +81-80-9472-5668.
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E-mail address:
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Corresponding author.
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Email:
[email protected] (Fu J.)
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Abstract To investigate the potential environmental effects of pond farming for Apostichopus
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japonicas in Yellow River estuary, we examined discrepancies of distance-based typical
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pollution indicators (TOC, TN, NO3-, NH4+, NO2- and PO43-) and biochemical tracers (δ13C
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and δ15N) in water column and sediment, as well as dietary characteristics of dominant
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macrobenthos between farming and non-farming areas. The results revealed that studied
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variables in water column showed no uniform spatial differences. Meanwhile, those in
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sediments displayed similar decrease tendencies from farming pond to the adjacent tidal
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flat, which was considered to represent the environmental effects of farming. Biochemical
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tracers (δ13C and δ15N) in both water column and sediment confirmed the origin of organic
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matters from the aquaculture waste. The detectable dispersion distance of aquaculture waste
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was restricted to an area within 50 m distance as determined by most variables in sediment
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(TOC, TN, NO3- and NH4+), particularly by C/N ratio and δ13C with which origins of the
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wastes were traced. Bayesian mixing models indicated that in the farming area BMA had a
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larger contribution, while POM(marine) showed a smaller contribution to the diets of Helice
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tridens and Macrophthalmus abbreviates compared to those in the non-farming area. The
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overall results showed that pond farming for Apostichopus japonicus in the Yellow River
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estuary altered the local environment to a certain extent. For methodological consideration,
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sediment biogeochemical characteristics as a historical recorder much more effectively
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reflected aquaculture waste accumulation, and stable isotope approaches are efficient in
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tracing the origin and extent of various allogenous sources.
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Keywords: sea cucumber farming; Apostichopus japonicus; environmental impact;
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aquaculture waste; stable isotope; Yellow River estuary
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1. Introduction Coastal aquaculture has rapidly grown in recent years with an increasing demand for
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seafood production (Naylor et al., 2000; Tanner and Fernandes, 2010). The expansion of
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coastal aquaculture, however, has raised widespread concerns on environmental costs and
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sustainable aquaculture techniques (Pusceddu et al., 2007; Tanner and Fernandes, 2010;
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Troell et al., 2003).
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Aquaculture farming release large amounts of dissolved and particulate nutrients
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originating from excretion process of reared organisms and uneaten feed and fecal materials
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(Holby and Hall, 1991; Lupatsch and Kissil, 1998). The redundant particulate organic
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carbon and nitrogen loads result in organic enrichment and sediment biogeochemical
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characteristics alternation (Brooks and Mahnken, 2003; Holby and Hall, 1991; Sarà et al.,
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2004). The excess dissolved inorganic nutrients that easily dispersed in the water and
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deposited in the sediment are the most concerned contaminant contributed to coastal
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eutrophication (Paerl, 2006; Smith et al., 1999; Wu et al., 2014).
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In recent years, sea cucumber farming has increased tremendously encouraged by the
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strong and growing market demand (Chen, 2003; FAO, 2008; Han et al., 2016) for its huge
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profit and high nutritional and pharmaceutical values (Bordbar et al., 2011). Consequently,
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much attention had been recently focused on its culturing manner for better production and
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development (Dong et al., 2010; Liu et al., 2010; Paltzat et al., 2008; Yokoyama, 2013).
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Although a variety of studied related to sea cucumber industry, such as hatchery production,
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culturing manner, sociological issues of stocking, were conducted in previous studies (Han
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et al., 2016; Ren et al., 2012), there existed a relative paucity of details concerning the
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environmental effects of sea cucumber farming activities (Li et al., 2014; Ren et al., 2010;
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Zheng et al., 2009). One of the most important reason probably lies in that most sea
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cucumbers are deposit feeders. They always are considered as the “environmental cleaners
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or scavengers (Yang et al., 1999) and therefore no exogenous nutrients were introduced.
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Actually, the exogenous nutrients input mainly depend on culturing manners. In most areas
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of China, commercial feeds which mainly composed of fish meal and soybean meal
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(accounting for 15% and 30% of total weight, respectively) were commonly used in sea
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cucumber ponds. The excessive feeds always make a large amount of dissolved and
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particulate waste which result in nutrients enrichment of water and sediment in the pond
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and adjacent environment.‐
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Sea cucumber farming in China increased tremendously along coasts, and the yield
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reached 205,791 tons (dry weight) in 2015 (China Fisheries Yearbook, 2016). For the
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present study area Yellow River estuary, sea cucumber production exponentially increased
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in the past ten years. The farming ponds area increased from 21.3 ha in 2003 to 21734 ha in
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2014 with an annual production of 11241 tons (dry weight) annually. Among the sea
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cucumber species cultured (nearly ten species) around the world at present, Apostichopus
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japonicus was the largest species in production, which was mainly artificially cultured in
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China (Han et al., 2016; Purcell et al., 2012). The annual hatchery production of juvenile of
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this species was approximately 6 billion in China, and the sum exceeded the total
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production of all other reared sea cucumber species (Purcell et al., 2012).‐The rapid growth
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of Apostichopus japonicus farming promoted a number of studies focusing on genetics (Li
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et al., 2009), energetic (Liu et al., 2009), neurology (SEO and LEE, 2011) and feeding
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ecology and physiology (Sun et al., 2013) etc. However, few studies reported the impacts
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of Apostichopus japonicus farming on pond sediment (Ren et al., 2012; Zheng et al., 2009)
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or coastal waters (Kang and Xu, 2016), no comprehensive investigation of water, sediment
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and biological characteristics was conducted to date .
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Environmental variables selected in this research were all universally recognized by
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previous studies. Some of them were specially emphasized as highly informative
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descriptors of heterotrophic loadings from aquaculture, such as dissolved inorganic
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nutrients (DIN) like nitrite (NO3-), nitrate (NO2-), ammonium (NH4+) and phosphate (PO43-)
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in water (Sarà, 2007), available phosphorus (AP) and acid volatile sulphide (AVS) in
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sediment (Yokoyama, 2003a). Other studied variables like total organic carbon (TOC) and
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total nitrogen (TN) has been widely used as important indices to represent organic matter
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contents in water and sediment (Gao et al., 2012). Similarly, C/N ratio, δ13C and δ15N were
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commonly utilized in recent years as effective tools to trace and verify the origin, extent
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and fate of nutrient sources in the environment and ecosystem (Andrews et al., 1998b;
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Sakamaki et al., 2010). The combination of these biogeochemical indicators will
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sufficiently describe and interpret the local environment variations (Kollongei and Lorentz,
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2014) induced by aquaculture activities.
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The main purposes of this study were to (1) examine the impacts of sea cucumber pond
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discharge on local tidal flat by evaluating spatial differences (between farming and
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non-farming area or among stations with a gradient distance to cultured pond) of
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environmental variables in water and sediment; (2) detect differences in feeding strategies
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of dominant macrobenthos between farming and non-farming areas. We hypothesized that
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significant spatial differences exist for contents of selected environmental variables by
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aquaculture activities with varying degrees. Besides, stable isotopes serve as an effective
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tool for revealing the source and fate of aquaculture induced organic matters in the
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environment.
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2. Materials and methods
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2.1 Study area
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This study was conducted in the north part (37°50′ – 38°10′ N, 118°20′ – 119°00′ E) of
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the Yellow River estuary (Fig. 1b) which is located in the northeast of Shandong Province,
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the People’s Republic of China (Fig. 1a). The study site is characterized by a
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warm-temperate continental monsoonal climate, with mean annual temperature and annual
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mean precipitation of 11.9 oC and 640 mm, respectively (Gao et al., 2014).
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A total area of 66.67 km2 was constructed for sea cucumber ponds along the north coast
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of Yellow River estuary. Each pond was constructed with 3 to 6 ha in size and 1.5 to 3m in
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depth. Apostichopus japonicus are cultured in ponds with a density of 10 individuals m-2.
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Commercial feed (fish meal 15%, soybean meal 20%, peanut flour 10% and seaweed meal
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40%) is added to ponds 1-2 times daily. Water exchange is managed uniformly twice a
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month when spring tide occurs. Pond influent water is taken from one tidal creek using
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pumping systems. Pond effluents are released into unified drainage channels then
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discharged into adjacent wetland directly through a sluice gate.
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The tidal flat (C-tidal flat) adjacent to the sea cucumber pond area is separated from sea
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cucumber ponds by a 7 m width road. It is dominated by salt marsh plant Suaeda salsa and
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two macrobenthic species, Helice tridens (omnivore) and Macrophthalmus abbreviates
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(omnivore) which are widely distributed in this locality. In order to eliminate potential
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effects of farming activities, we investigated the environments of a tidal flat (N-tidal flat) in
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Natural reserve area (non-farming area) which is 15 km departing from the C-tidal flat and
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was strictly considered with similar natural characteristics (such as sediment type, tidal
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characteristics, elevation).
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2.2 Field sampling and laboratory procedures
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Field sampling was conducted in the sea cucumber pond area, C-tidal flat and N-tidal
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flat from May 1 to May 21, 2016. In the C-tidal flat, there were no other allochthonous
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nutrients loading except aquaculture effluent. In the sea cucumber pond area, two stations
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(Fig. 1c) were positioned in the sea cucumber pond and effluent channel, respectively. Two
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gradient-based sampling were set up in C-tidal flat and N-tidal flat. Six stations (Fig. 1c) in
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the C-tidal flat creek were set at 0 m, 50 m, 100 m, 200 m, 500 m and 800 m distances from
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the sluice gate using an electronic distance meter (Nikon COOLSHOT 40i Laser
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Rangefinders) and a GPS receiver (Garmin GPS map64s). Four stations (Fig. 1d) in the
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N-tidal flat creek were set at 0 m, 50 m, 100 m and 200 m distances from the reclaimed
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land. Other two stations (CM and NM) were set at the coastal area of C-tidal flat and
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N-tidal flat (Fig. 1c, d) for water analysis (organic analysis only).
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At each station (except for CM and NM stations), both water and sediment samples
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were collected for analyses of TOC, TN, C/N ratio, δ13C and δ15N, and dissolved inorganic
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nutrients (NO3-, NO2-, NH4+ and PO43-/ AP). The chemical items analyzed in this study
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were shown with their abbreviations in Table 1. Dominant consumers and primary
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producers were sampled at 0 m, 50 m and 100 m stations in each tidal flat. All of these
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samplings were conducted in the ebb tides.
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At each station, three replicate water samples were collected by a bucket from the
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surface layer of the water for the analyses of particulate organic matter (POM) and
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dissolved inorganic nutrients. Besides, marine POM (POM(marine)) in CM and NM stations
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were collected at the sea surface of each adjacent offshore. POM samples were collected by
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filtering 0.4 to 3 L surface seawater through a 125 µm mesh (to remove zooplankton)
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followed by glass-fiber filters (Whatman GF/F, φ47 mm). To remove carbonates, POM
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samples were treated with 1.2 N HCl overnight and then oven dried at 60 oC for stable
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isotope analysis. The remained filtered water samples were treated immediately for nutrient
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analysis using SAN++ continuous flow analyzer (SKALAR, Netherlands).
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To detect the biogeochemical characteristics of deposited sediment, three replicate
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surface layer (0-1 cm) sediments were collected by using a 48 mm (inner diameter) acrylic
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tube. An aliquot of the samples was used for the analysis of acid volatile sulphide (AVS)
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concentration to present sediment condition affected by aquaculture (Yokoyama, 2010).
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The remainder was divided into two groups (group A and group B). Group A was used for
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elemental (TOC & TN) and stable isotope analysis (δ13C & δ15N), group B was used for the
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analyses of dissolved inorganic nutrients in sediment. Sediment samples of group A were
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weighed, dried at 60 oC for 72 h，then weighed again to determine the water content, and
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ground to a fine powder. Prior to analyses, samples for carbon isotope analysis were soaked
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in 1.2 N HCl overnight to remove carbonates, filtered on a Nuclepore polycarbonate
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track-etch membrane filter (pore size = 0.2 µm) and dried again. Aliquots of samples not
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soaked in 1.2 N HCl were used for nitrogen isotopic analysis. Group B samples used for
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dissolved inorganic nutrients analysis were air-dried at room temperature and passed
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through a 2 mm mesh sieve. Samples for NO3- and NH4+ analysis (5.0 g) were extracted
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with 2 mol l-1KCL (25 ml), and samples for AP (1.2 g) were extracted with 0.5 mol l-1
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NaHCO3 (25 ml) for determining the exchangeable nitrogen and phosphorous. After shock
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extraction for 30 min, the samples were filtered and analyzed by SAN++ continuous flow
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analyzer to determine the concentrations of NO3-, NH4+ and AP.
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Benthic microalgae (BMA) samples (n = 3) were extracted from the surface sediment in
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each tidal flat (Couch, 1989; Riera and Richard, 1996). The sediment was brought back to
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the laboratory within 30 min and then spread on a flat tray (1 cm depth) and covered by a
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nylon screen (63 µm mesh). A 4 to 5 mm layer of combusted sand (acid-washed and burned,
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125-500 µm) was spread over the nylon screen. The sand was kept wet using filtered
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seawater and then cultured under fluorescent light for 12 to 15 h. The top 2 mm of sand was
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then removed and filtered through a 63 µm fiber sieve by washing with distilled water.
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Water containing the microalgae was filtered onto GF/F (φ25 mm) filters, and oven dried at
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60 oC for stable isotope analysis.
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The dominant salt marsh plant Suaeda salsa was collected by hand. Leaves from 10
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plants were taken as a single sample. Plant debris was collected by hand nets. The dominant
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macrobenthic animals (two crabs) were caught by a long-handled scoop around the
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sampling station within 5 m distance. Each sample was then preserved on ice and returned
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to the laboratory for sorting and sampling. Muscle tissue of crab was taken for animal
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isotope samples. All animal and plant samples were first treated with 1.2 N HCl to remove
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carbonates and then oven dried at 60 oC and ground to fine powder for isotope analysis.
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The elemental and isotope analysis of samples were analyzed using a Delta XP plus
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isotope ratio mass spectrometer linked to a Conflo IIII interface (Thermo Fisher Scientific,
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USA) with a Flash EA1112 elemental analyzer
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Stable isotope ratios were expressed in the typical estuary notation (δ13C or δ15N),
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defined by the following equation:
δ13C or δ15N = (Rsample/Rstandard – 1) ×103
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and R = 13C/12C or 15N/14N
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The standard reference materials were Pee Dee Belemnite (PDB) and atmospheric N2
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for carbon and nitrogen isotopes, respectively.
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2.3 Data analysis
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Principle components analysis (PCA) (Manly, 1986) was conducted in R V3.3.1 to
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explore the relationships among stations that driven by sediment and water variables using
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the function prcomp and princomp of R package stats. To evaluate spatial differences in
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N-tidal flat, C-tidal flat, effluent channel and sea cucumber pond, a multivariate technique
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was applied using the PRIMER 6.0 ecological software package developed by the
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Plymouth Marine Laboratory. Ordination was computed via hierarchical cluster analysis
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(CLUSTER) based on the Euclidean distance of studied environmental variables and
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isotopic compositions. Seriation test and SIMPER (Similarity percentages) analysis were
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performed to analyze the community structure. One-way ANOSIM was performed to test
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the difference in the studied areas. Multiple comparisons of each environmental variable between stations were conducted
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to detect spatial differences between farming and non-farming areas or among stations with
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a gradient distance to the cultured pond. As no gradient change was observed in the N-tidal
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flat along the distance (0 m, 50 m, 100 m, 200 m), the data from different sampling stations
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were calculated together to represent a reference location expressed as N-tidal flat.
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Bonferroni test (Rice, 1989) was used when data is normally distributed (Shapiro and
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Wilk, 1965) and homoscedastic (Bartlett, 1937). While Kruskal-Wallis test (Kruskal and
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Wallis, 1952) was performed when the data is not normally distributed or homoscedastic,
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followed by Gao post hoc test (Gao et al., 2008; Konietschke et al., 2015) to compare
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differences among stations for the selected environmental variable. Statistical significance
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level was considered at 5%. Statistical analyses were performed using the function
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Wilk-Shapiro test, Bartlett’s test, Bonferroni test and Kruskal test of the R package stats
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and the function Gao of the R package nparcomp (Ihaka and Gentleman, 1996; Shirley,
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1977) and oneway_test function of the R package coin (Ihaka and Gentleman, 1996) in R
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V3.3.1.
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Carbon and nitrogen stable isotopes of primary producers (food sources) and consumers
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were initially investigated using biplot diagrams. Because no differences in stable isotope
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composition of consumers and primary producers were found along the distance gradient in
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N-tidal flat and C-tidal flat, each of them was calculated together in each tidal flat. A t-test
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was performed to test significant differences in stable isotope values of consumers and
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primary producers. The Bayesian stable isotope mixing model, SIAR v 4.0 (Stable Isotope
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Analysis in R) (Parnell et al., 2010) was used to determine proportional contributions of
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potential food sources to dominant consumers. Contributions of dietary sources were
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represented as mean and 95% credibility interval (Ci). The fractionations δ13C and δ15N for
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crustacean (muscle, acid treated) calculated from raw data presented in Yokoyama et al.
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(2005) were δ13C = 1.93 ± 0.40‰, δ15N = 3.97 ± 0.49‰, respectively.
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3. Results
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3.1 Environmental variables
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Each sampling location (sea cucumber pond, effluent channel, C-tidal flat and N-tidal
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flat) were clearly separated from each other by PCA analysis (Fig. 2a) with seventeen water
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and sediment variables (Table 1). The first three principal components (PC1, PC2 and PC3)
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explained 73% of the total variance. The main contributions to PC1 included sediment
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variables TOC(S), TN(S), CN(S), δ13C(S), NO3-(S), NH4+ (S), PO43- (S) (with large positive
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coefficients), δ15N(S) and δ13C(POM) (with negative coefficient). Vector plots of sediment
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variables and δ13C(POM) showed that PC1 represented axes of increasing organic
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accumulation and nutrients concentration in sediment and decreasing δ15N(S) and δ13C(POM)
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from tidal flat to effluent channel and then followed by sea cucumber pond. As for PC2
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(Fig. 2b), POM variables (except for δ13C(POM)) and PO43-(water) in water were closely related
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to PC2 as indicated by large positive coefficients (TOC(POM), TN(POM), CN(POM), δ15N(POM))
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and negative coefficient (PO43-(water)), respectively. PC2 represented axes of increasing
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organic accumulation in POM and decreasing PO43-(water) concentration from sea cucumber
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pond area to N-tidal flat. The main contribution to PC3 (Fig. 2c) was from DIN(water) that
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showed highly positive coefficients (NO3-(water), NO2-(water), NH4+(water)). As the vector plots
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of DIN(water) that PC3 represented, however, no obvious gradient variation was found across
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stations.
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The cluster analysis (Fig.3) showed that studied localities could be distinguished into
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three groups (sea cucumber pond, the combination of C-tidal flat and effluent channel,
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N-tidal flat) with the threshold Euclidean distance more than 7.0 (Fig. 3). The results of the
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pairwise test in ANOSIM showed that significant differences were present between N tidal
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flat and sea cucumber pond (R = 1, P < 0.01), N tidal flat and the combination of C tidal
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flat and effluent channel (R = 0.529, P < 0.01), sea cucumber pond and the combination of
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C tidal flat and effluent channel (R = 0.991, P < 0.01). With the subsequent SIMPER
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analysis, the primary contributors to three groups were TOC(s) (89.60% contribution),
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TOC(water) (63.16% contribution) and TOC(s) (97.82% contribution).
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3.2 Water and sediment biogeochemical characteristics
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3.2.1 Water biogeochemical characteristics
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TOC(POM) and TN(POM) concentrations (Figs. 4a, b) detected in farming area (mean ± SD
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= 0.07 ± 0.02 and 0.05 ± 0.2 mg l-1, respectively) were significantly lower than those in
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non-farming area (1.4 ± 0.6 and 0.2 ± 0.1 mg l-1, respectively). C/N ratios of POM (Fig. 4c)
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ranged from 4.7 to 9.1 and showed significant differences between sea cucumber pond and
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N-tidal flat (F = 9.350, P < 0.001). δ13C(POM) in sea cucumber pond (-20.6 ± 1.0‰),
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effluent channel (-19.9 ± 1.2‰) and C-marine stations (-21.1 ± 0.1‰) showed significantly
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lower values than those in C-tidal flat (-16.7 ± 1.6‰), N-tidal flat (-16.9 ± 1.2‰) and
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N-marine (-18.4 ± 0.4‰) stations (Fig. 4d). δ15N(POM) values in the sea cucumber pond
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(-2.0 ± 0.2‰) was significantly lower than those in other stations (F = 38.06, P < 0.001).
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No remarkable differences were found within C-tidal flat stations (0.9 ± 0.6‰).
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NO3- and NH4+ showed no significant differences (F = 1.90, P > 0.05; F = 0.62, P >
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0.05) among stations (Figs. 4f, h), whereas significant differences were present in NO2- and
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PO43- (X² = 21.85, DF = 8, P < 0.01; X² = 28, DF = 8, P < 0.001) (Fig. 4g, i). The NO2-
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concentration in sea cucumber pond (53 ± 2 µg l-1) was clearly higher than that in N-tidal
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flat (40 ± 9 µg l-1), but no distinct differences were found among stations in the farming
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area or between C-tidal flat and N-tidal flat. The PO43- concentrations in sea cucumber
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pond (32 ± 1 µg l-1), effluent channel (33 ± 1 µg l-1) and C-tidal flat (29 ± 3 µg l-1) were
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clearly higher than that in N-tidal flat (20 ± 2 µg l-1), but no distinct differences were found
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within stations in the farming area.
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3.2.2 Sediment biogeochemical characteristics AVS concentration detected in sea cucumber pond was 0.1 ± 0.5 mg g-1 (dry sediment)
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higher than those in the effluent channel, C-tidal flat and N-tidal flat (all less than 0.01 mg
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g-1). For TOC(S) and TN(S), significant differences (X² = 25.61, DF = 8, P < 0.01; X² =
306‐
31.246, DF = 8, P < 0.001) were observed among stations (Figs. 5a, b). TOC(S) and TN(S) in
307‐
the sea cucumber pond (5.9 ± 1 mg g-1 and 0.8 ± 0.3 mg g-1, respectively) and effluent
308‐
channel (2.95 ± 0.43 mg g-1 and 0.51 ± 0.08 mg g-1, respectively) were significantly higher
309‐
than those in the C-tidal flat (1.2 ± 0.3 mg g-1 and 0.3 ± 0.1 mg g-1, respectively) and
310‐
N-tidal flat (1.8 ± 0.2 mg g-1 and 0.3 ± 0 mg g-1, respectively). Both TOC(S) and TN(S)
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311‐
contents showed decreasing trends from sea cucumber pond to 0 m station, and no gradient
312‐
variations along the dispersal distance in C-tidal flat. C/N ratios (Fig. 5c) in the pond, effluent channel, 0 m station and N-tidal flat had
314‐
significant differences from other stations in C-tidal flat (50 m, 100 m, 200 m and 800 m).
315‐
A decreasing trend was found from pond to 0 m. δ13C(S) and δ15N(S) values of sea cucumber
316‐
pond were remarkably different from the effluent channel, C-tidal flat and N-tidal flat
317‐
stations (Fig. 5d, e). A decreasing trend was found from pond to 0 m station. δ13C(S) of 0 m
318‐
station showed a large range of standard deviation and no significant differences with the
319‐
effluent channel and other stations in C-tidal flat and N-tidal.
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No significant differences (Fig. 5f) among stations were found for NO3-(S) (X² = 11.982,
320‐
DF = 8, P > 0.05). Both NO3-(S) and NH4+(S) were found a decreasing trend from pond to 0
322‐
m station. The concentration of NH4+(S) ranged from 1.8 to 18.1 mg kg-1 (Fig. 5g) and
323‐
varied across stations (X² = 27.671, DF = 8, P < 0.001). The highest NH4+(S) concentration
324‐
found in sea cucumber pond (15.8 ± 2.0 mg kg-1) was significantly higher than those from
325‐
effluent channel (8.6 ± 2.9 mg kg-1), C-tidal flat (5.7 ± 2.6 mg kg-1) and N-tidal flat stations
326‐
(4.3 ± 0.5 mg kg-1). NH4+(S) content in effluent channel showed no significant differences
327‐
with C-tidal flat stations (except for 500m), but was obviously higher than that in N-tidal
328‐
flat. A gradient decrease was observed from pond to 0 m station. AP contents (Fig. 5h) in
329‐
pond (8.2 ± 0.9 mg kg-1) and effluent channel (10.4 ± 3.9 mg kg-1) were significantly higher
330‐
than those in both C-tidal flat (3.6 ± 0.6 mg kg-1) and N-tidal flat (3.4 ± 2.1 mg kg-1)
331‐
stations (F = 7.00, P < 0.001).
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3.3 Feeding strategies of dominant macrobhenthos δ13C value of H. tridens in the C-tidal flat (-13 ± 0.6‰) was significantly higher (t-test,
334‐
P < 0.01) than that in the N-tidal flat (-15.5 ± 3.3‰) (Table. 2). δ15N value of H. tridens in
336‐
C-tidal flat (4.3 ± 1.1‰) was significant than that in N-tidal flat (5.8 ± 0.4‰). As for the
337‐
crab M. abbreviates, δ13C values were similar (t-test, P > 0.05) between C-tidal flat (-12.1 ±
338‐
0.6‰) and N-tidal flat (-12.3 ± 0.4‰), whereas δ15N values in C-tidal flat (2.1 ± 0.7‰)
339‐
was significantly lower (t-test, P < 0.01) compared to that in N-tidal flat (3.8 ± 0.9‰).
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In according with the presumptive trophic shift of crustaceans (1.9 ± 0.4‰ in δ13C and
340‐
4.0 ± 0.5‰ in δ15N) presented by Yokoyama et al. (2005), the expected δ13C and δ15N
342‐
values of the H. tridens diet would be -17.4 ± 3.7‰ and 1.8 ± 0.9‰, respectively in N-tidal
343‐
flat, and -14.9 ± 1.0‰ and 0.3 ± 1.6‰, respectively in C-tidal flat. The expected δ13C and
344‐
δ15N values of M. abbreviates diet would be -17.4 ± 3.7‰ and 1.8 ± 0.9‰, respectively in
345‐
N-tidal flat, and -14.9 ± 1.0‰ and 0.3 ± 1.6‰, respectively in C-tidal flat.
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Among the primary producers, salt marsh plant Suaeda salsa had the lowest δ13C
347‐
(around -29.7 ± 1.3 to -30.4 ± 0.5‰ ) and highest δ15N (around 6.1 ± 0.9 to 6.3 ± 1.5‰)
348‐
values. There were large differences in isotope compositions between Suaeda salsa and
349‐
hypothetical diet of the two crabs. The δ13C values of hypothetical diet for two crabs were
350‐
between POM (POM(marine), POM(tidal flat) and POM(pond)) and BMA (Fig. 6).
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SIAR analysis result (Fig. 7a, b) revealed that in N-tidal flat, the contributions of
351‐ 352‐
POM(marine) to the diet of H. tridens (25%, Ci 0 – 49%) was similar to those of BMA (27%,
353‐
Ci 8 – 45%) and plant debris (25%, Ci 8 – 42%) (Fig. 7a), while POM(tidal flat) (19%, Ci 0 –
354‐
37%) and salt marsh plant Suaeda salsa (4%, Ci 0 – 9%) made minor contributions. In
16‐ ‐
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C-tidal flat, POM(marine) made a smaller contribution to the diet of H. tridens (3%, Ci 0 –
356‐
7%), while BMA made a greater contribution (45%, Ci, 36 – 54%) (Fig. 7b). Besides, the
357‐
contributions of POM(tidal flat) (15%, Ci 0 – 32%), plant debris (18%, Ci 0 – 36%) and
358‐
Suaeda salsa (1%, Ci 0 – 2%) were relatively low. In addition, POM(pond) contributed 19%
359‐
(Ci 2 – 34%) of the diet of H. tridens in C-tidal flat.
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As for crab M. abbreviates (Fig. 7c, d), BMA overwhelmingly dominated the diet in
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N-tidal flat (51%, Ci 39 – 63%) and C-tidal falt (54%, Ci 37 – 69%). But same with H.
362‐
tridens, POM(marine) showed the much lower contribution to the diet of M. abbreviates in
363‐
C-tidal flat (6%, Ci 0 – 16%) than that in N-tidal flat(22%, Ci 0 – 44%). Likewise, POM(tidal
364‐
flat)
365‐
contribution to the diet of M. abbreviates in both N-tidal flat and C-tidal falt. But, POM(pond)
366‐
contributed a relatively lower amount (9%, Ci 0 – 22%) to the diet of M. abbreviates than it
367‐
contributed to H. tridens in C-tidal flat.
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and plant debris made small contributions, and Suaeda salsa made a minimal
4． ．Discussion
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4.1 Differences of distance-based environmental variables and their implications
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Overall, this study intended to evaluate the environmental impacts of sea cucumber
371‐ 372‐
farming based on two kinds of analyses. The first one was the detection of discrepancies
373‐
among distance-based pollution indicators in the water column and sediment. The second
374‐
one was the utilization of biochemical tracers (δ13C and δ15N) to trace the origin of organic
375‐
matters. Both methods had been widely confirmed as reliable tools to evaluate
376‐
environmental effects of aquaculture (Gowen and Bradbury, 1987; Sarà, 2007; Sarà et al.,
17‐ ‐
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2004; Wai et al., 2011). A total of eight typical environmental variables (TOC, TN, C/N,
378‐
AVS, NO3-, NH4+, NO2-, PO43- and AP) were studied in this paper. For the studied
379‐
parameters in the water column, they showed no uniform variation tendencies. For example,
380‐
TOC(POM), TN(POM), NH4+(water) and NO3-(water) contents were similar between farming and
381‐
non-farming areas, or among stations within the farming area (such as pond and its adjacent
382‐
tidal flat). NO2-(water) and PO43-(water) contents in the farming area were higher than those in
383‐
N-tidal flat, while they did not differ among farming areas (Fig. 4). In our study,
384‐
distribution patterns of studied parameters may have many potential reasons. One possible
385‐
reason lies in that eutrophication, related to the rapid development of mariculture
386‐
(Bouwman et al., 2013) or other anthropogenic activities, are widely present in the Yellow
387‐
River estuary and its adjacent waters (Chen et al., 2013; Kang and Xu, 2016; Zhang et al.,
388‐
2008). High background values hinder the statistical differences.
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In pervious studies, NO3-(water) was recognized as the typical dominant part of DIN in
390‐
the studied coastal waters (Gong, 2012; Zhang et al., 2008). But our study found the same
391‐
result as Kang (2016), that NH4+(water) represented the relative major proportion of DIN than
392‐
NO3-(water) in the mariculture zone. This result corresponded well to the meta-analysis of
393‐
Sarà (2007) that NH4+(water) appears to be the most affected compound by aquaculture
394‐
loadings than NO2-(water), NO3-(water) and followed by PO43-(water). However, discrepancies
395‐
among distribution patterns of studied parameters prevented us from drawing much more
396‐
clear conclusions.
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Despite the distinct distribution patterns of nutrients in the water column, NO3-(S),
397‐ 398‐
NH4+(S), AP(S), TOC(S), TN(S) and AVS contents in sediment showed a uniform
18‐ ‐
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distance-based differences. They had significantly higher values in farming area than those
400‐
in non-farming areas. Within the farming area, a decrease transect from pond to 0 m station
401‐
was detected. These findings clearly demonstrated the presence of aquaculture waste from
402‐
the sea cucumber farming, and additionally implied a restricted dispersion distance
403‐
(approximately 50 m from farming pond to 0 m station in C-tidal flat) of aquaculture waste
404‐
that accumulated in sediment. It was also worth noting that although AVS content in sea
405‐
cucumber pond in this study was below the threshold (0.2 mg/g) in some rules ensuring a
406‐
sustainable aquaculture (Uede, 2008; Yokoyama, 2003b; Yokoyama et al., 2007), the
407‐
content was much higher than those reported from other natural sediments. Similar cases
408‐
were also found in TOC(S) and TN(S) concentration in sea cucumber pond. This phenomenon
409‐
were also considered as results of sea cucumber aquaculture.
411‐
4.2 Detection of origins of organic matters and their implications
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In the water column, the result of C/N ratios(water) and isotopic composition analysis
412‐
revealed that sea cucumber pond had different organic origins from adjacent tidal flat and
414‐
non-farming area. For example, C/N ratios(POM) detected in pond, 0 m, 50 m, 100 m and
415‐
500 m stations were close to the Redfield ratio (C/N=106:16) for marine plankton
416‐
(Andrews et al., 1998a; Burkhardt et al., 1999; Redfield, 1963), which might indicate that
417‐
the main contributor of POM in these stations was marine phytoplankton . The relatively
418‐
higher values of C/N ratios(POM) in the effluent channel, 200 m, 800 m and N-tidal flat
419‐
suggested that POM resulted from a mixture of marine phytoplankton and other organic
420‐
matter sources. Low C/N ratios(POM) detected in N-marine and C-marine areas might be
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attributed to the contribution of marine zooplankton with low C/N ratio (Båmstedt, 1986;
422‐
Walve and Larsson, 1999). The lowest δ13C(POM) and δ15N(POM) values found in sea
423‐
cucumber pond maybe due to the allogenous sources. Terrestrial plant (soybean and peanut)
424‐
and seaweed flour (seaweed cultured in offshore) introduced by aquaculture (commercial
425‐
feeds) had low δ13C and δ15N values, and the presence of allogenous sources resulted in low
426‐
isotopic values. In this case, the distribution pattern of δ13C(POM) and δ15N(POM) values
427‐
indicated the environmental impact of aquaculture.
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In the sediments, high δ13C(S) and low δ15N(S) values were detected in sea cucumber
428‐
pond. Similar to the reason of low δ15N(POM) compositions in the water column, low δ15N(S)
430‐
values should be due to the allogenous organic matter input. δ13C(S) in pond sediment was
431‐
approximately equal to δ13C(POM) of POM , which suggested that they had homogenous
432‐
resources. In the adjacent and natural tidal flat, deposition of organic matter from salt marsh
433‐
plant (δ13C = 29.74 ± 1.31, δ15N = 6.1 ± 0.91) contributed to the sediment with much lower
434‐
δ13C and high δ15N values. Therefore, high δ13C and low δ15N values in the pond were a
435‐
reflection of the disturbance of aquaculture waste.
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4.3 Effects of pond farming waste on food sources for dominant macrobenthos
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SIAR analysis results revealed that BMA was the most important food resources for
438‐ 439‐
H. tridens and M. abbreviates in both farming and non-farming area. However, for both
440‐
species, contributions of BMA to the diet in the C-tidal flat were bigger than those in
441‐
N-tidal flat. This founding might imply that nutrient enrichment of surface sediment may
442‐
enhance the primary production of BMA which furthermore enabled a higher contribution
20‐ ‐
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to the consumer's diet. Meanwhile, the contribution of POM(marine) in C-tidal flats was lower
444‐
than that in N-tidal flats and the POM(pond) showed opposite tendencies. This differences
445‐
indicated that sea cucumber farming activities might modify the dietary characteristics of
446‐
benthic animals to a certain extent. The similar modifications of food composition of
447‐
dominant benthic animals by aquaculture were confirmed in previous studies of other
448‐
aquaculture type, such as fish farming (Mazzola and Sarà, 2001; Yokoyama and Ishihi,
449‐
2007) and shrimp pond farming (Kon et al., 2009).
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4.4 Implications for operation and management of sea cucumber farming The overall conclusion of the present study was that sea cucumber farming slightly
452‐
altered the local environment, however, this modification was restricted to a limited area
454‐
(within 50 m). Up to present, there existed a relative paucity of details concerning the
455‐
environmental effects of sea cucumber farming (Li et al., 2014; Ren et al., 2010; Zheng et
456‐
al., 2009). Therefore, it’s hard to make a comparison of our results with the previous
457‐
studies. In this study, the short dispersal distance might be attributed to the ideal operation
458‐
mode and management of sea cucumber pond farming in the studied region. Firstly, the
459‐
main feed for Apostichopus japonicus is composed of much seaweed flour (40%) and less
460‐
protein feeds (fishmeal = 15%) in comparison with the shrimp feed (around 45%) (Dierberg
461‐
and Kiattisimkul, 1996; Paez-Osuna, 2001; Trott et al., 2004), that reduced the
462‐
accumulation of organic matter from the waste feed. Besides, the intense of sea cucumber
463‐
farming activities was moderate, with low densities (10 individuals m-2) that far below the
464‐
typical densities (20 individuals m-2) practiced by Chinese farmers (Dong et al., 2010; Kang
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and Xu, 2016). In addition, waste waters from sea cucumber ponds, connected by internal
466‐
pipes, were gathered in large channels and drained intermittently by valves, which may
467‐
result in a higher deposition before the discharge. The modification in sediment was
468‐
restricted to 50 m distance, which indicates that farming pond and effluent channel were
469‐
heavily disturbed. Consequently, high natural feeds content, low stocking densities, and line
470‐
effluent channels reduced the deleterious effects of sea cucumber farming on the adjacent
471‐
environment. Besides, deposited sludge should be more effiently removed physical efforts
472‐
or by filters like salt marsh plant, bivalves, microalgae.
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For the management of aquaculture activities, one of the challenge is to develop
474‐
practical, cost-effective monitoring techniques that reflect the impact and processes
475‐
(Burford et al., 2003). The present study provides meaningful information about
476‐
environmental impacts of Apostichopus japonicus farming. The unique characteristics of
477‐
Apostichopus japonicus farming discharges indicated that the predominant effects of sea
478‐
cucumber farming discharge were in the sediment processes, rather than water column
479‐
processes. In this case, environmental variables should be unequally treated.
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In conclusion, the present study considered that pond farming for Apostichopus
481‐
japonicus in the Yellow River estuary altered local environment to a certain extent, as
482‐
determined by the discrepancies among distance-based environmental parameters in
483‐
sediment (TOC(S), TN(S) and AVS, NO3-(S), NH4+(S), AP(S)) as well as isotopic signatures
484‐
(δ13C and δ15N). Such kind of modification was restricted to 50 m distance (covers farming
485‐
pond and effluent channel). Apostichopus japonicus farming also modified dietary
486‐
characteristics of some dominant macroinvertebrates. For methodological consideration,
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sediment biogeochemical characteristics as a historical recorder much more effectively
488‐
reflected both organic and dissolved inorganic nutrients induced by aquaculture.
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489‐ Acknowledgements
491‐
This study was supported financially by National Key Basic Research Programme of China
492‐
(2013CB430406). We acknowledge experimental support by Center for Ecological Research of Kyoto
493‐
University. We are grateful to the great support from the lab of Aquatic Environmental Biology, Kyoto
494‐
University. We thank Dr. Edouard Lavergne for the idea of statistical analyses in environment variables.
495‐
And thank Prof. Yoh Yamashita, Dr. Bunei Nishimura and Dr. Zou Yuxuan for their assistance in
496‐
different aspects of this study.
498‐ 499‐
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667‐ Fig. 1. Map of the study area showing sampling stations in sea cucumber farming area (sea cucumber
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Fig. 2. Principal component analysis (PCA) based on the seventeen sediment and water parameters.
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Fig. 3. Dendrogram showing cluster relationship between samples. Fig. 4. Comparisons of water parameters. Fig. 5. Comparisons of sediment parameters.
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Fig. 6. Stable isotope biplots of δ13C and δ15N for dominant consumers and their potential food sources in N-tidal flat (a) and C-tidal flat (b). Fig. 7. Proportionate contribution of potential food sources to the diet of Helice tridents and Macrophthalmus abbreviates by SIAR boxplots.
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Table 1. Chemical items analyzed in the present study and their abbreviations. Water
Particulate organic matter in the pond area
Sediment
POM(pond)
in the tidal flat area POM(tidal flat) in the coastal area
POM(marine) TOC(POM)
TOC(S)
Total nitrogen
TN(POM)
TN(S)
Carbon to nitrogen ratio
C/N ratio(POM) C/N ratio(S)
Stable carbon isotope ratio
δ13C(POM)
δ13C(S)
Stable nitrogen isotope ratio δ15N(POM)
δ15N(S)
Nitrate
NO3-(water)
Nitrite
NO2-(water)
Ammonium
NH4+(water)
Phosphate Acid volatile sulfides
PO43-(water)
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Table 2 Carbon and nitrogen isotopic compositions of consumers and their potential food sources in the tidal flat of Natural Reserve Area and sea cucumber pond area N-tidal flat C-tidal flat δ13C (‰) δ15N (‰) n δ13C (‰) δ15N (‰) n Consumers / producers a a b Helice tridens -15.5 ± 3.3 5.8 ± 0.4 12 -13.0 ± 0.6 4.3 ± 1.1b 21 Macrophthalmus abbreviates -12.3 ± 0.4a 3.8 ± 0.9a 10 -12.1 ± 0.6a 2.1 ± 0.7b 7 a a b POM (marine) -18.4 ± 0.3 1.5 ± 0.3 3 -21.1 ± 0.1 2.4 ± 0.3b 3 a a a b POM (tidal flat) -16.9 ± 1.2 2.7 ± 0.4 12 -16.7 ± 1.6 0.9 ± 0.6 18 POM (pond) -20.6 ± 0.9 -2.0 ± 0.2 3 BMA -8.1 ± 0.4a 2.2 ± 0.4a 3 -10.7 ± 1.5a 3.3 ± 0.4b 3 a a a a Salt marsh plant -30.4 ± 0.5 6.3 ± 1.5 9 -29.7 ± 1.3 6.1 ± 0.9 9 Plant debris -25.8 ± 0.9a 0.7 ± 0.4a 3 -17.7 ± 1.6b -0.1 ± 0.4a 3 The mean ± SD values are given for food items with the number of samples (n) ≥ 3. BMA: benthic microalgae. Different letters (a, b) on stable isotope values indicate significant difference (P < 0.05) by t-test.
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Fig. 1. Map of the study area showing sampling stations (sample numbers from 1 to 36) in four areas. Sea cucumber pond area: sea cucumber pond (pond), effluent channel (EC); C-tidal flat area: 0 m to 800 m; N-tidal flat area: 0 m to 200 m; farming coastal area (CM); non-farming coastal area (NM).
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Fig. 2. Principal component analysis (PCA) based on the seventeen sediment and water parameters. (a) 3D scores plot (PC1, PC2 and PC 3) of the environmental variables to the samples. (b) PC1 and PC2. (c) PC1 and PC3.
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Fig. 3. Dengrogram showing cluster relationship between samples. Different sampling locations were shown in different colors of solid circle (green = N-tidal flat, blue = C-tidal flat, yellow = effluent channel, red = sea cucumber pond). See Fig. 1 for the sample numbers.
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Fig. 4. Comparisons of water parameters. Different letters above bars indicate significant differences (p＜0.05) by Bonferroni and Gao test. Boxes represent 25%-75% percentiles; range bars represent the 5% and 95% percentiles, solid dots in the boxes represent mean values, hollow dots represent values outside of 95% confidences interval.
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Fig. 5. Comparisons of sediment parameters. Different letters above bars indicate significant differences (p＜ 0.05) by Bonferroni test and Gao test. Boxes represent 25%-75% percentiles; range bars represent the 5% and 95% percentiles, solid dots in the boxes represent mean values, hollow dots represent values outside of 95% confidences interval.
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Fig. 6. Stable isotope biplots of δ13C and δ15N for dominant consumers and their potential food sources in N-tidal flat (a) and C-tidal flat (b). Grey solid dots: expected isotope composition of the diet of each crab. Error bars represent SD.
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Fig. 7. Proportionate contribution of potential food sources to the diet of Helice tridents and Macrophthalmus abbreviates by SIAR boxplots. Plant: salt marsh plant. Debris: plant debris. Dark gray, gray and light gray boxes show 50, 75, 95 credibility interval respectively.
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Highlights
Sediment biogeochemical characteristics are effective indices for estimation of sea cucumber pond aquaculture impact Dispersion distance of aquaculture waste is restricted within a distance
Stable isotope approaches are efficient in tracing aquaculture waste in environment
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