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Integrated response in taxonomic diversity and eco-exergy of macrobenthic faunal community to artificial reef construction in Daya Bay, China
Ecological Indicators 101 (2019) 512–521
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Integrated response in taxonomic diversity and eco-exergy of macrobenthic faunal community to artificial reef construction in Daya Bay, China ⁎
Quan Chena, , Huarong Yuana, Pimao Chena,b,
T
⁎
a Key Laboratory of Marine Ranch Technology, Chinese Academy of Fishery Sciences; Scientific Observing and Experimental Station of South China Sea Fishery Resources and Environment, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology and Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, PR China b South China Sea Fisheries Research Institute Shenzhen Test Base, Chinese Academy of Fishery Sciences, Guangzhou 510300, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Community structure Thermodynamic indicator Comparison analysis Marine resources
The degradation of marine habitats (especially coral reefs) and the loss of marine fauna can be mitigated by the construction of artificial reefs (ARs). Macrobenthic fauna are considered excellent indicators of how disturbance or succession affects ecosystems, but the integrated effects of AR construction on taxonomic and thermodynamic indicators of macrobenthic faunal communities have seldom been studied. In the current study, we investigated changes in the taxonomic diversity and ecological exergy (eco-exergy) of the macrobenthic faunal community for 2 years following AR construction in Daya Bay, China. The results indicated that macrobenthic faunal diversity, species richness, and evenness increased but abundance, biomass, eco-exergy, and specific eco-exergy decreased following AR construction; after declining, however, abundance, biomass, and eco-exergy appeared to be increasing at the end of the 2-year sampling period. In terms of biomass and eco-exergy, mollusks were the dominant group at each sampling period and were mainly responsible for the changes in biomass and ecoexergy. Eco-exergy was positively correlated with macrobenthic abundance and was negatively related with evenness. Effects of AR construction on the nearby non-reef habitat were similar to those on the AR habitat. These results indicate that long-term assessment of multiple ecological indicators at diverse study areas is needed to determine the effects of AR construction on marine biological resources.
1. Introduction Since the beginning of the current century, marine defaunation has been accelerating exponentially because of both natural and anthropogenic causes (McCauley et al., 2015). This defaunation will likely damage global social economics and human well-being (Hollowed et al., 2013; Barange et al., 2014). Coastal ecosystems are frequently more influenced by humans than deep-water or pelagic ecosystems presumably because of their ease of access (Halpern et al., 2008). Coral reefs, one of most productive and biologically diverse coastal habitats, are particularly important fisheries and tourist attractions but also help protect coasts and have cultural value (Cinner et al., 2013; Beck et al., 2018). Because coral reefs are sensitive to increases in temperature, they are severely deteriorating in response to global warming (Selig et al., 2012; Messmer et al., 2017), ocean acidification, and man-made stresses (Hoegh-Guldberg et al., 2007). For these reasons, coastal reefs have often been the focus of marine defaunation research (Graham
et al., 2011). Given the extensive deterioration of coral reefs worldwide, artificial structures are urgently needed to rehabilitate marine environments and associated biological resources (Shin et al., 2014; Ng et al., 2017). Artificial reefs (ARs) are human-made structures that are placed on coastal seabeds to mimic some characteristics of a natural reef and to thereby rehabilitate coastal habitats (Baine, 2001; Perkol-Finkel et al., 2006). Since the 1930s, the use of ARs has increased worldwide and is now common in North America, Europe, and Japan (Santos et al., 2011). Although ARs may be constructed for many reasons, the primary goal is frequently to aggregate fish and ensure fishery sustainability (Claudet and Pelletier, 2004; Seaman, 2007; Feary et al., 2011). Many studies have documented that ARs can support greater fish diversity, abundance, species richness, and biomass than nearby natural reefs, but the structure of the fish community might differ in ARs vs. natural reefs, mainly because of differences in the dominant species (Feary et al., 2011; Folpp et al., 2013; Lowry et al., 2014; Mills et al., 2017). The
⁎ Corresponding authors at: South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences 231 West Xingang Road, Haizhu District, Guangzhou 510300, PR China. E-mail addresses: [email protected] (Q. Chen), [email protected] (P. Chen).
https://doi.org/10.1016/j.ecolind.2019.01.049 Received 6 November 2018; Received in revised form 6 January 2019; Accepted 22 January 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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community structure of fish may differ between ARs and surrounding natural reefs even after many years (Perkol-Finkel et al., 2006). In addition to affecting fish, AR construction also inevitably alters the benthic environment, local sediment deposition, and benthic invertebrate communities (Perkol-Finkel et al., 2006; Feary et al., 2011). In recent years, researchers have paid increasing attention to the effects of AR on non-swimming organisms in general and on benthic fauna in particular (Fukunaga and Bailey-Brock, 2008). Benthos are fauna that usually live at the bottom of water bodies during all or most of their life history, and those benthos that cannot pass through a 1.0-mm sieve are commonly referred to as macrobenthic fauna (Hu et al., 2009). Macrobenthic fauna are sensitive to their environment and can be used to indicate changes in environmental quality and ecosystem health (Perus et al., 2007). Because macrobenthic fauna can indicate current and historic ecosystem stress, researchers have used the community structure of macrobenthic fauna to assess the recovery or successional status of freshwater and coastal water wetlands (Banerjee et al., 2017; Hu et al., 2018; Linares et al., 2018a,b; Tang et al., 2018). Ecosystem responses to change in environment are not always indicated only by changes in the relative abundance or taxonomic diversity of organisms (Hooper et al., 2005). Understanding responses to disturbance or the dynamics of succession often requires the integrated analysis of multiple ecological indicators, including more holistic ecological indicators (Salas et al., 2006). Although evaluation of taxonomic biodiversity and community composition is useful for assessing the effects of perturbation on ecosystem health, a more holistic assessment can be obtained by measuring ecological exergy (eco-exergy), i.e., by measuring how far an ecosystem has moved away from thermodynamic equilibrium (Jørgensen, 2006; Jørgensen and Ulanowicz, 2009). In combination with other indicators, eco-exergy has recently been widely used to reveal the response of macrobenthic fauna to environmental changes and for determining how stress influences ecosystem health in marine or freshwater ecosystems (Marchi et al. 2012; Veríssimo et al., 2017; Chen et al., 2018b, 2018c, 2019; Linares et al., 2018a,b). In the current research, we measured the effect of AR construction on the taxonomic diversity and eco-exergy of the macrobenthic fauna community in Daya Bay, Shenzhen, China. We attempted to answer the following questions: (1) How do macrobenthic faunal taxonomic diversity and eco-exergy (including specific eco-exergy) change after AR construction in Daya Bay, China? (2) Do taxonomic diversity and ecoexergy provide similar or different information about the effects of AR construction? This study differs from our previous report concerning the effects of AR construction on macrobenthic fauna (Chen et al., 2019) in that it includes a different sampling schedule (seasonal effects were emphasized in the previous study but not in the current study), a shorter sampling period following AR construction, and a different study area in the South China Sea.
been increasingly threatened by extinction mainly because of pollution and overfishing. To restore the marine ecological environment and fishery resources, the Agriculture, Forestry and Fishery Bureau of Shenzhen announced the creation of an artificial reef in Daya Bay in 2007. The AR, which was constructed in April 2007 by the South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, consists of 376 blocks (length × width × height: 3 m × 3 m × 5 m) of reinforced concrete (Chen et al., 2013). The total artificial reef area is 2.65 km2, and its location in Daya Bay is indicated in Fig. 1. Two types of habitat were selected: artificial reef (AR) habitat and nearby non-reef control (NC) habitat. For the AR habitat, we randomly selected six sampling sites (AR1, AR2, AR3, AR4, AR5, and AR6) on the reef (Fig. 1). For the NC habitat, we also selected six sampling sites; the NC sites, which were was used to reveal the effect of AR construction on the surrounding natural habitats, were paired with the AR sampling sites (NC1, NC2, NC3, NC4, NC5, and NC6) and were located near but not on the reef (Fig. 1). The AR and NC sites were located in the same maritime space, experienced the same general environment, and differed only in proximity to the AR. Although our previous study (Chen et al., 2019) was also conducted in South China Sea, the study areas differed in the two studies. The previous study was conducted in the Pearl River Estuary, whereas the current study was conducted in southwest portion of Daya Bay, as noted above. As a consequence, the sampling sites differed between the two studies. 2.2. Investigation of macrobenthic fauna The macrobenthic fauna were sampled in April 2007, immediately before the AR was constructed, and for 2 years after AR construction, i.e., March 2008 (spring), May 2008 (summer), August 2008 (autumn), November 2008 (winter), and May 2009. On each of the six sampling dates, samples were simultaneously collected from both the AR and NC sites. On each sampling date, two sediment blocks were collected at each sampling site with a 0.1-m2 bottom grab sampler. The sediment was washed, stirred manually, and poured gently through a 1.0-mm sieve. The macrobenthic organisms retained on the sieve were stored in an alcohol solution (> 75% in volume ratio). The macrobenthic fauna were counted with the aid of a dissecting microscope and were identified to species with the help of standard taxonomic references (Huang and Lin, 2012). The macrobenthic faunal were dried to a constant weight in a vacuum oven, and the dry weight of each species was determined for calculation of eco-exergy. The specimens from two sediment blocks per site were pooled, leading to six replicates per habitat per sampling date. The procedures for sample collection and data analysis followed the standards specified for oceanographic-marine biological survey (China National Standardization Management Committee, 2007).
2. Materials and methods 2.3. Calculation of macrobenthic faunal taxonomic diversity indices
2.1. Study area and site
The following formulas were used to characterize the macrobenthic faunal communities at each site:
The study was conducted at the southwest portion of Daya Bay, inshore of Dapeng Peninsula, Shenzhen, South China (22o33′00′’22o34′08′’ N, 114o33′21′’-114o34′59′’ E, Fig. 1). Daya Bay is located in a subtropical region and covers 600 km2 in the Shenzhen marine area. The bay water depth ranges from 6 to 16 m. The mean annual air temperature is 22.0 °C, the mean coldest monthly temperature is 15.0 °C, the mean hottest monthly temperature is 33.5 °C, and the mean seawater surface temperature is 28.0 °C. The minimal sea surface temperature usually occurs in winter (average 17.3 °C), and the maximum occurs in summer (average 29.3 °C). Daya Bay contains diverse natural habitats, including coral reefs, mangrove forests, rock reefs, sandy shores, and mudflats (Wang et al., 2008). Since the 1980s, fishery resources in the Shenzhen marine area have
(1)
Dominance Y= (N i/ N )fi S
Shannon - Weiner diversity index H' = −
∑ (P i)(ln P i) i= 1
(2)
S
Simpson's diversity index D = 1 −
∑ (P i)2 i= 1
513
(3)
Simpson's diversity index D' = (S − 1)/lnN
(4)
Pielou's evenness index J′ = H′/lnS
(5)
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Fig. 1. Locations of the artificial reef (AR) and sampling sites in Daya Bay, China. There were six AR sampling sites, which were on the reef, and six non-reef control sites (NC), which were near but not on the reef.
where S is the total number of species, N is the total number of individuals, Ni is the number of species i, fi is the frequency of species i in each plot, and Pi is the proportion of individuals in a sample that belongs to species i. The Shannon-Weiner diversity index mainly contains information about the number and relative abundance of each species, and Simpson’s diversity index emphasizes the value of dominants in the community.
values used in this study were obtained from the Animal Genome Size Database (http://www.genomesize.com/index.php). For those species not in that database, their mean C-value for their genus, family, or order was used (Fu, 2015). The specific eco-exergy (SpEx) of the macrobenthic faunal community (i.e., the eco-exergy per unit mass of the fauna) was computed using Eq. (8):
2.4. Calculation of macrobenthic faunal eco-exergy and specific eco-exergy
⎛ SpEx = ⎜18.7 kj g−1 × ⎝
The eco-exergy (Ex) of the macrobenthic faunal community (i.e., the eco-exergy of the fauna per unit area) was computed using Eq. (6):
∑ Ci βi i=0
⎠
i=0
(8)
(6) 2.5. Statistical analysis
−1
is the mean energy of detritus or dead organic where 18.7 kj g matter; Ci is the biomass of the ith species (g m−2); and βi is the weighting factor of the genetic information based on Kullbach’s measurement of genome size and the complexity of the ith species (Jørgensen et al., 1995; Fu, 2015; Lu et al., 2015). Two methods (termed SWF and CV) are usually used to calculate the β value. In the SWF method, the suggested β value is used directly (Table S1; Jørgensen et al., 2005; Jørgensen and Nielsen, 2007). In the CV method, the β value is calculated from the C-value, which is the amount of DNA contained within a haploid nucleus as measured by the number of base pairs (bp) (Fu, 2015; Lu et al., 2015). In the CV method, the weighting factor β is calculated by Eq. (7):
β = 1 + ln(20165000000c)/7.43 × 105
i=0
where all factors and variables (Ci and βi) are defined in the same manner as those in Eq. (6).
n
Ex = 18.7 kj g−1 ×
n
n
∑ Ci βi⎞⎟/ ∑ Ci
One-way analysis of variance (ANOVA) was conducted to determine the differences in the taxonomic indices and eco-exergy of the macrobenthic faunal community among the six sampling periods in the same habitat. Independent-samples t tests were used to determine the differences in taxonomic indices and eco-exergy of macrobenthic faunal community between the two habitats. Paired-samples t tests were conducted to compare macrobenthic faunal eco-exergy and specific ecoexergy calculated by the SWF method vs. the CV method (ExSWF vs. ExCV and SpExSWF vs. SpExCV). Significance was set at p < 0.05. The similarities in the composition of macrobenthic faunal communities between the habitats were determined using the Bray-Curtis similarity coefficient for hierarchical cluster analysis (group-average linking method). Univariate regressions were used to analyze the relationships between the taxonomic indices and eco-exergy of the macrobenthic faunal community. The ANOVA, t tests, and regression analysis were performed using SPSS 20.0 (SPSS for Windows, IBM, Armonk, New York, USA). Cluster analysis was performed with Plymouth Routines in Multivariate Ecological Research (PRIMER) 4.5.
(7)
where 7.43 × 105 is the contribution of detritus to the eco-exergy in g/ l, and c is the total quantity of DNA in the diploid genome of the faunal cell in picograms (pg; 1 pg = 0.98 × 109 base pairs). Multiplication of c in pg by 1.65 × 108 is indicates the number of nucleotide triplets, which is the maximum coding capacity of the haploid genome given that each adjacent triplet of nucleotides corresponds to a transcribed RNA signal (Fonseca et al., 2000; Graur and Li, 2000). Twenty is the number of possible amino acids that can be coded by each triplet. All C514
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Fig. 2. Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, Pielou’s evenness, abundance, and biomass of the macrobenthic faunal community in AR and NC habitats in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Values are means ± SE. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
3. Results
after AR construction (Fig. 2E and F). Among the macrobenthic faunal groups, biomass and abundance were highest for mollusks regardless of habitat and sampling period. Biomass and abundance were often significantly lower for mollusks after AR construction (Table 2 and Table S2). Based on the Bray-Curtis similarity metric (group-average linking method) of macrobenthic faunal abundance data (fourth root, standardized), macrobenthic faunal assemblage patterns did not differ between the two kinds of habitats (Fig. 3). After AR construction, the diversity of dominant species in both habitats increased, and the dominance of mollusks gradually decreased (Table 1). In general, the macrobenthic taxonomic indices at any sampling time did not significantly differ between AR and NC habitats (Table S4).
3.1. Taxonomic diversity of the macrobenthic faunal community following artificial reef construction A total of 83 species and 1358 individuals of macrobenthic fauna, representing 7 phyla, 11 classes, and 55 families, were recorded from the two habitats (Table S2). The individuals belonged to seven phyla: mollusks (28 species), arthropods (9 species), annelids (36 species), Echinodermata (6 species), Chordata (2 species), Nemertea (1 species), and Echiura (1 species). These seven phyla represented 76.0, 1.7, 16, 3.7, 1.6, 0.5, and 0.5% of the 1358 individuals, respectively. Compared to the values obtained before the AR was constructed, the macrobenthic faunal Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, and Pielou’s evenness indices increased, but the abundance and biomass decreased after AR construction in AR habitats (p = 0.023, 0.050, 0.009, 0.048, 0.322, and 0.470, respectively) and in NC habitats (p = 0.001, 0.001, 0.006, 0.001, 0.012, and 0.137, respectively) (Fig. 2). All macrobenthic faunal taxonomic indices were frequently lower in winter than in other seasons of 2008 (Table S3; p = 0.023, 0.135, 0.062, 0.982, 0.335, and 0.014, respectively, compared with spring; p = 0.015, 0.115, 0.025, 0.270, 0.593, and 0.090, respectively, compared with summer; p = 0.041, 0.027, 0.033, 0.288, 0.957, and 0.247, respectively, compared with autumn). Values from the last sampling period (May 2009) suggested that macrobenthic faunal abundance and biomass might be beginning to increase 2 years
3.2. Eco-exergy of the macrobenthic faunal community following artificial reef construction Compared to their pre-construction values, macrobenthic faunal eco-exergy values (ExSWF and ExCV) decreased after AR construction in both AR and NC habitats (Fig. 4A and B); the values on the last sampling date, however, suggested that ExSWF and ExCV might be increasing in both kinds of habitats 2 years after the AR was constructed. Macrobenthic faunal specific eco-exergy (SpExSWF and SpExCV) decreased after AR construction in both AR and NC habitats. Among the macrobenthic faunal groups, ExSWF and ExCV were highest for mollusks regardless of habitat and sampling period. ExSWF and 515
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Fig. 3. Similarity of macrobenthic faunal communities in AR and NC habitats in Daya Bay, China as indicated by Bray-Curtis similarity coefficients. The habitat abbreviations are explained in Fig. 1. The numbers following the habitat abbreviations refer to the six sampling sites in each habitat. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
(Fig. 5E and F). Macrobenthic taxonomic and thermodynamic indicators were closely correlated to each other.
ExCV for mollusks, however, tended to decline after AR construction (Table 2). When compared for each sampling period, macrobenthic eco-exergy values (ExSWF and ExCV) and specific eco-exergy values (SpExSWF and SpExCV) were seldom significantly different between the two habitats (Table S4). Regardless of habitat or sampling period, macrobenthic faunal ExCV was significantly higher than macrobenthic faunal ExSWF (p < 0.001) (Table S5).
4. Discussion An integrated system of indicators can provide a comprehensive evaluation of ecological health and a reliable theoretical basis for environmental management (Tang et al., 2013, 2015, 2018). Veríssimo et al. (2017), for example, reported that both exergy-based and traitbased indices coherently reflected the effects of environmental change on benthic macroinvertebrates. Secondary production and eco-exergy were useful for revealing the response macrobenthic faunal assemblages to canopy cover conditions in Neotropical headwater streams, and suggested that thermodynamic indicators could help guide the decision making of environmental managers (Linares et al., 2018a).
3.3. Relationship between taxonomic indices and eco-exergy of the macrobenthic faunal community Based on linear regression, macrobenthic faunal Pielou’s evenness was negatively related to the ExSWF (Fig. 5D), and macrobenthic faunal abundance and biomass were positively related to ExSWF and ExCV
Table 1 The dominant species (Y > 0.01) in artificial reef and non-reef control habitats at each sampling time in Daya Bay, China. The April 2007 sampling time was before reef construction, and all other sampling times were after artificial reef construction. Sampling time
Artificial reef
Non-reef control
April 2007 March 2008 May 2008 August 2008 November 2008
Periglypta lacerata, Aglaophamus lyrochaeto, Capitella capitata Periglypta lacerata, Amphioplus laevis, Terebra dussumieri Lovenia subcarinata, Nothria holobranchiata, Amphioplus laevis, Aglaophamus lyrochaeto Glycera alba, Aglaophamus lyrochaeto, Sternaspis scutata Periglypta lacerata, Aglaophamus lyrochaeto, Aliculastrum cylindrica
May 2009
Babylonia areolata, Periglypta lacerata, Terebellides stroemii
Periglypta lacerata Periglypta lacerata, Lumbrineris heteropoda, Minolia chinensis Periglypta lacerata, Aglaophamus lyrochaeto, Lovenia subcarinata, Nothria holobranchiata Amphioplus laevis, Aglaophamus lyrochaeto, Glycera alba Aglaophamus lyrochaeto, Periglypta lacerata, Parachaeturichthys polynema, Turricula nelliae spurius Listriolobus brevirostris, Babylonia areolata, Aglaophamus lyrochaeto
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Table 2 The biomass and eco-exergy (values and proportions) of macrobenthic faunal groups at each sampling time in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Because biomass and eco-energy values did not significantly differ between reef sites (AR) and non-reef control sites (NC), values are averages across all sites. Sampling time
Category
Biomass(g m−2)
Proportion
ExSWF (kj m−2)
Proportion
ExCV (kj m−2)
Proportion
April 2007
Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total
1.75 565.25 3.15 21.40 0.00 0.00 0.00 591.55 66.65 242.55 6.45 4.00 0.00 12.10 0.00 331.75 14.10 288.20 8.45 274.15 0.00 0.00 0.00 584.90 7.10 39.20 9.50 21.50 0.00 0.00 2.30 79.60 0.70 26.60 1.35 2.00 0.00 2.10 0.00 32.75 0.60 32.65 12.90 18.25 3.45 0.00 263.65 331.50
0.0030 0.9555 0.0053 0.0362 0.0000 0.0000 0.0000 1.0000 0.2009 0.7311 0.0194 0.0121 0.0000 0.0365 0.0000 1.0000 0.0241 0.4927 0.0144 0.4687 0.0000 0.0000 0.0000 1.0000 0.0892 0.4925 0.1193 0.2701 0.0000 0.0000 0.0289 1.0000 0.0214 0.8122 0.0412 0.0611 0.0000 0.0641 0.0000 1.0000 0.0018 0.0985 0.0389 0.0551 0.0104 0.0000 0.7953 1.0000
7.59E + 03 3.14E + 06 7.83E + 03 1.28E + 05 0.00E + 00 0.00E + 00 0.00E + 00 3.28E + 06 2.89E + 05 1.37E + 06 1.60E + 04 2.39E + 04 0.00E + 00 5.57E + 04 0.00E + 00 1.76E + 06 6.12E + 04 1.60E + 06 2.10E + 04 1.64E + 06 0.00E + 00 0.00E + 00 0.00E + 00 3.33E + 06 3.08E + 04 2.18E + 05 2.36E + 04 1.29E + 05 0.00E + 00 0.00E + 00 5.16E + 03 4.06E + 05 3.04E + 03 1.48E + 05 3.36E + 03 1.20E + 04 0.00E + 00 9.66E + 03 0.00E + 00 1.76E + 05 2.60E + 03 1.87E + 05 3.21E + 04 1.09E + 05 8.58E + 03 0.00E + 00 5.92E + 05 9.31E + 05
0.0023 0.9563 0.0024 0.0390 0.0000 0.0000 0.0000 1.0000 0.1647 0.7809 0.0091 0.0136 0.0000 0.0317 0.0000 1.0000 0.0184 0.4819 0.0063 0.4934 0.0000 0.0000 0.0000 1.0000 0.0759 0.5364 0.0582 0.3169 0.0000 0.0000 0.0127 1.0000 0.0172 0.8411 0.0190 0.0679 0.0000 0.0548 0.0000 1.0000 0.0028 0.2006 0.0345 0.1173 0.0092 0.0000 0.6356 1.0000
1.03E + 05 9.74E + 06 7.52E + 04 2.32E + 05 0.00E + 00 0.00E + 00 0.00E + 00 1.01E + 07 5.59E + 06 4.65E + 06 1.97E + 05 1.07E + 05 0.00E + 00 1.29E + 05 0.00E + 00 1.07E + 07 1.07E + 06 5.12E + 06 1.41E + 05 2.80E + 06 0.00E + 00 0.00E + 00 0.00E + 00 9.13E + 06 4.17E + 05 6.68E + 05 1.76E + 05 3.35E + 05 0.00E + 00 0.00E + 00 6.47E + 04 1.66E + 06 4.11E + 04 5.80E + 05 2.47E + 04 5.37E + 04 0.00E + 00 2.36E + 04 0.00E + 00 7.23E + 05 3.52E + 04 7.76E + 05 2.23E + 05 2.08E + 05 4.91E + 04 0.00E + 00 7.41E + 06 8.70E + 06
0.0101 0.9596 0.0074 0.0229 0.0000 0.0000 0.0000 1.0000 0.5237 0.4358 0.0184 0.0101 0.0000 0.0120 0.0000 1.0000 0.1173 0.5609 0.0154 0.3064 0.0000 0.0000 0.0000 1.0000 0.2512 0.4021 0.1060 0.2018 0.0000 0.0000 0.0389 1.0000 0.0568 0.8022 0.0341 0.0742 0.0000 0.0326 0.0000 1.0000 0.0040 0.0892 0.0256 0.0239 0.0056 0.0000 0.8516 1.0000
March 2008
May 2008
August 2008
November 2008
May 2009
habitats subjected to intermediate levels of disturbance (Townsend et al., 1997; Flöder and Sommer, 1999). ARs may also enrich the benthic assemblages in nearby habitats and contribute to increases in regional biodiversity (Carvalho et al., 2013; Chen et al., 2019). Macrobenthic abundance and biomass, however, might decrease soon after the disturbance of AR construction (Townsend et al., 1997; Flöder and Sommer, 1999). The effects of ARs on the benthic communities might change with AR age (Moura et al., 2007). Liu et al. (2017) found that the structure of the benthic macroinvertebrate community was stable in a 3-year period after AR construction and that benthic macroinvertebrate biomass increased with reef age. Long-term studies have confirmed that ARs frequently increase the abundance, species richness, and biomass of the benthic faunal community (Nicoletti et al., 2007; Brown et al., 2014). After initially declining following AR construction in the current study, macrobenthic faunal abundance and biomass also tended to increase at the end of the 2-year sampling period. In a previous study, AR construction increased benthic habitat heterogeneity, which led to increases in macrobenthic faunal species richness and diversity (Tews
Based on taxonomic and eco-exergy indicators, researchers have concluded that natural succession and invasion by the exotic cordgrass Spartina alterniflora threaten the macrobenthic faunal community in mangrove forests in Zhanjiang, China (Chen et al., 2018b,c). Before the current study, however, researchers had seldom explored the changes in integrated indicators of the macrobenthic faunal community following AR construction. In the present study, we found that the biodiversity, species richness, and evenness of the macrobenthic faunal community increased but that the abundance, biomass, and eco-exergy decreased on an AR and in the surrounding area during the 2-year period following AR construction. These results are different from those of a similar study conducted in the Pearl River Estuary, China, which found that macrobenthic faunal diversity, species richness, abundance, biomass, and ecoexergy all tended to increase after AR construction (Chen et al., 2019). These differences may result in part from the different locations used in the two studies. The results obtained with the AR in the current study are consistent with the intermediate disturbance hypothesis, which states that the taxon richness of macroinvertebrates will be highest in 517
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Fig. 4. Eco-exergy (ExSWF and ExCV) and specific eco-exergy (SpExSWF and SpExCV) of the macrobenthic faunal community in two habitats in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Values are means ± SE. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
faunal community following AR construction. Regardless of habitat or sampling period, the eco-exergies were significantly lower if calculated by the suggested β value (SWF) than by the C-value (CV). This mainly resulted from the different biological information included in the two calculations: the SWF method emphasizes species evolution level, and the CV method emphasizes species genetic information (Jørgensen, 2015; as indicated by the latter author, both methods have advantages). Whether based on the suggested β value or C-value, the eco-exergies of the macrobenthic faunal community significantly decreased during the 2-year period following AR construction in Daya Bay, China. We infer that AR construction at least initially reduced the stability and resilience of the macrobenthic faunal community.
et al., 2004). The hard texture of AR, however, makes it a difficult habitat for macrobenthic fauna, especially mollusks (Kristensen and Kostka, 2005; Wang et al., 2014). Considering that mollusks were the dominate group in term of abundance and biomass in the current study, the decrease in mollusk abundance and biomass led to the decline in the total abundance and biomass of the macrobenthic faunal community. Although the abundance and biomass of the macrobenthic faunal community tended to increase at the end of the 2-year sampling period following AR construction, determining whether these indices will continue to rise will require additional research. Like total abundance and biomass, the eco-exergy and specific ecoexergy of the macrobenthic faunal community declined during the first 2 years following AR construction in the present study. The changes in the dominant macrobenthic groups as the AR aged might help explain the changes in the eco-exergy of the macrobenthic faunal community. Although construction of the AR tended to increase the biomasses of non-mollusk macrobenthic groups in the current study, the construction significantly reduced mollusk biomass, and the suggested β value and Cvalue were higher for mollusks than for arthropods, annelids, and other groups (Table S1 and http://www.genomesize.com/index.php; Tang et al., 2013). As a consequence, the eco-exergy of the macrobenthic faunal community decreased during the 2-year sampling period of this study. That changes in macrobenthic faunal abundance were also positively related to changes in macrobenthic faunal eco-exergy in our study is reasonable because abundance and biomass are usually correlated for macrobenthic faunal communities (Baldrighi et al., 2014; Eklöf et al., 2017; Chen et al., 2019; and R2 = 0.58 and p = 0.004 in the current study) and because biomass is used in the calculation of ecoexergy. However, changes in macrobenthic faunal evenness were negatively related to changes in macrobenthic faunal eco-exergy, perhaps because macrobenthic species with larger body mass are more likely to have a random or aggregated distribution (Smith, 1980; Dolbeth et al., 2014; Vedenin et al., 2015). We also found a negative correlation between the biomass and evenness of the macrobenthic faunal community in the current study (R2 = 0.35, p = 0.042). We used two methods (SWF and CV) to measure the dynamics of the eco-exergies (eco-exergy and specific eco-exergy) of the macrobenthic
4.1. Limitations of this study Given that the effects of AR construction on macrobenthic fauna might change over time, a more complete understanding of the effects of ARs on marine organisms will clearly require long-term monitoring (Liu et al., 2017; Ng et al., 2017). Our understanding of AR effects would also benefit from the integrated assessment of additional macrobenthic faunal indicators, such as functional groups and production (Chen et al., 2018a; Linares et al., 2018a). Considering that the results of the current study differed from the results of our previous study (Chen et al., 2019), which was conducted in a different location in the South China Sea, future studies should include data from multiple study areas with and without AR construction. 5. Conclusions During the 2-year period following AR construction, the diversity, species richness, and evenness of the macrobenthic faunal community increased in both the AR habitat and the surrounding habitat, but the abundance, biomass, and eco-exergy of the community decreased; a possible increase in the latter properties was evident, however, in the last sampling period. Macrobenthic eco-exergy was closely correlated with abundance, biomass, and evenness but not with diversity or species richness. These results suggest that the use of multiple ecological 518
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Fig. 5. Linear relationships between ExSWF and ExCV and the Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, Pielou’s evenness, abundance, and biomass of the macrobenthic faunal community in Daya Bay, China. Equations, coefficients of determination, and probabilities are presented only when the linear regressions were significant (p < 0.05).
and with identification of the macrobenthic fauna. We also thank Dr. Zhifeng Wu at the Institute of Urban Environment, Chinese Academy of Sciences for help in making the map and Prof. Bruce Jaffee for editing the English grammar. This work was funded by the Central Public-interest Scientific Institution Basal Research Fund, South China Sea Fisheries Research Institute, CAFS (2017YB25) and the Shenzhen Science and Technology Innovation Project (JCYJ20160331141759795).
indicators (including eco-exergy and at least one measure of diversity) and long-term sampling in multiple research areas are needed to assess the effects of AR construction on the macrobenthic faunal community. Author contributions QC conceived the idea and designed the study; QC, HRY, and PMC carried out the field measurements, laboratory analyses, and statistical analyses, and contributed to manuscript writing and revisions. All authors contributed critically to the drafts and gave final approval for publication.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.01.049.
Acknowledgements We sincerely thank all the people who assisted with field sampling 519
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Integrated response in taxonomic diversity and eco-exergy of macrobenthic faunal community to artificial reef construction in Daya Bay, China ⁎
Quan Chena, , Huarong Yuana, Pimao Chena,b,
T
⁎
a Key Laboratory of Marine Ranch Technology, Chinese Academy of Fishery Sciences; Scientific Observing and Experimental Station of South China Sea Fishery Resources and Environment, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology and Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, PR China b South China Sea Fisheries Research Institute Shenzhen Test Base, Chinese Academy of Fishery Sciences, Guangzhou 510300, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Community structure Thermodynamic indicator Comparison analysis Marine resources
The degradation of marine habitats (especially coral reefs) and the loss of marine fauna can be mitigated by the construction of artificial reefs (ARs). Macrobenthic fauna are considered excellent indicators of how disturbance or succession affects ecosystems, but the integrated effects of AR construction on taxonomic and thermodynamic indicators of macrobenthic faunal communities have seldom been studied. In the current study, we investigated changes in the taxonomic diversity and ecological exergy (eco-exergy) of the macrobenthic faunal community for 2 years following AR construction in Daya Bay, China. The results indicated that macrobenthic faunal diversity, species richness, and evenness increased but abundance, biomass, eco-exergy, and specific eco-exergy decreased following AR construction; after declining, however, abundance, biomass, and eco-exergy appeared to be increasing at the end of the 2-year sampling period. In terms of biomass and eco-exergy, mollusks were the dominant group at each sampling period and were mainly responsible for the changes in biomass and ecoexergy. Eco-exergy was positively correlated with macrobenthic abundance and was negatively related with evenness. Effects of AR construction on the nearby non-reef habitat were similar to those on the AR habitat. These results indicate that long-term assessment of multiple ecological indicators at diverse study areas is needed to determine the effects of AR construction on marine biological resources.
1. Introduction Since the beginning of the current century, marine defaunation has been accelerating exponentially because of both natural and anthropogenic causes (McCauley et al., 2015). This defaunation will likely damage global social economics and human well-being (Hollowed et al., 2013; Barange et al., 2014). Coastal ecosystems are frequently more influenced by humans than deep-water or pelagic ecosystems presumably because of their ease of access (Halpern et al., 2008). Coral reefs, one of most productive and biologically diverse coastal habitats, are particularly important fisheries and tourist attractions but also help protect coasts and have cultural value (Cinner et al., 2013; Beck et al., 2018). Because coral reefs are sensitive to increases in temperature, they are severely deteriorating in response to global warming (Selig et al., 2012; Messmer et al., 2017), ocean acidification, and man-made stresses (Hoegh-Guldberg et al., 2007). For these reasons, coastal reefs have often been the focus of marine defaunation research (Graham
et al., 2011). Given the extensive deterioration of coral reefs worldwide, artificial structures are urgently needed to rehabilitate marine environments and associated biological resources (Shin et al., 2014; Ng et al., 2017). Artificial reefs (ARs) are human-made structures that are placed on coastal seabeds to mimic some characteristics of a natural reef and to thereby rehabilitate coastal habitats (Baine, 2001; Perkol-Finkel et al., 2006). Since the 1930s, the use of ARs has increased worldwide and is now common in North America, Europe, and Japan (Santos et al., 2011). Although ARs may be constructed for many reasons, the primary goal is frequently to aggregate fish and ensure fishery sustainability (Claudet and Pelletier, 2004; Seaman, 2007; Feary et al., 2011). Many studies have documented that ARs can support greater fish diversity, abundance, species richness, and biomass than nearby natural reefs, but the structure of the fish community might differ in ARs vs. natural reefs, mainly because of differences in the dominant species (Feary et al., 2011; Folpp et al., 2013; Lowry et al., 2014; Mills et al., 2017). The
⁎ Corresponding authors at: South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences 231 West Xingang Road, Haizhu District, Guangzhou 510300, PR China. E-mail addresses: [email protected] (Q. Chen), [email protected] (P. Chen).
https://doi.org/10.1016/j.ecolind.2019.01.049 Received 6 November 2018; Received in revised form 6 January 2019; Accepted 22 January 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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community structure of fish may differ between ARs and surrounding natural reefs even after many years (Perkol-Finkel et al., 2006). In addition to affecting fish, AR construction also inevitably alters the benthic environment, local sediment deposition, and benthic invertebrate communities (Perkol-Finkel et al., 2006; Feary et al., 2011). In recent years, researchers have paid increasing attention to the effects of AR on non-swimming organisms in general and on benthic fauna in particular (Fukunaga and Bailey-Brock, 2008). Benthos are fauna that usually live at the bottom of water bodies during all or most of their life history, and those benthos that cannot pass through a 1.0-mm sieve are commonly referred to as macrobenthic fauna (Hu et al., 2009). Macrobenthic fauna are sensitive to their environment and can be used to indicate changes in environmental quality and ecosystem health (Perus et al., 2007). Because macrobenthic fauna can indicate current and historic ecosystem stress, researchers have used the community structure of macrobenthic fauna to assess the recovery or successional status of freshwater and coastal water wetlands (Banerjee et al., 2017; Hu et al., 2018; Linares et al., 2018a,b; Tang et al., 2018). Ecosystem responses to change in environment are not always indicated only by changes in the relative abundance or taxonomic diversity of organisms (Hooper et al., 2005). Understanding responses to disturbance or the dynamics of succession often requires the integrated analysis of multiple ecological indicators, including more holistic ecological indicators (Salas et al., 2006). Although evaluation of taxonomic biodiversity and community composition is useful for assessing the effects of perturbation on ecosystem health, a more holistic assessment can be obtained by measuring ecological exergy (eco-exergy), i.e., by measuring how far an ecosystem has moved away from thermodynamic equilibrium (Jørgensen, 2006; Jørgensen and Ulanowicz, 2009). In combination with other indicators, eco-exergy has recently been widely used to reveal the response of macrobenthic fauna to environmental changes and for determining how stress influences ecosystem health in marine or freshwater ecosystems (Marchi et al. 2012; Veríssimo et al., 2017; Chen et al., 2018b, 2018c, 2019; Linares et al., 2018a,b). In the current research, we measured the effect of AR construction on the taxonomic diversity and eco-exergy of the macrobenthic fauna community in Daya Bay, Shenzhen, China. We attempted to answer the following questions: (1) How do macrobenthic faunal taxonomic diversity and eco-exergy (including specific eco-exergy) change after AR construction in Daya Bay, China? (2) Do taxonomic diversity and ecoexergy provide similar or different information about the effects of AR construction? This study differs from our previous report concerning the effects of AR construction on macrobenthic fauna (Chen et al., 2019) in that it includes a different sampling schedule (seasonal effects were emphasized in the previous study but not in the current study), a shorter sampling period following AR construction, and a different study area in the South China Sea.
been increasingly threatened by extinction mainly because of pollution and overfishing. To restore the marine ecological environment and fishery resources, the Agriculture, Forestry and Fishery Bureau of Shenzhen announced the creation of an artificial reef in Daya Bay in 2007. The AR, which was constructed in April 2007 by the South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, consists of 376 blocks (length × width × height: 3 m × 3 m × 5 m) of reinforced concrete (Chen et al., 2013). The total artificial reef area is 2.65 km2, and its location in Daya Bay is indicated in Fig. 1. Two types of habitat were selected: artificial reef (AR) habitat and nearby non-reef control (NC) habitat. For the AR habitat, we randomly selected six sampling sites (AR1, AR2, AR3, AR4, AR5, and AR6) on the reef (Fig. 1). For the NC habitat, we also selected six sampling sites; the NC sites, which were was used to reveal the effect of AR construction on the surrounding natural habitats, were paired with the AR sampling sites (NC1, NC2, NC3, NC4, NC5, and NC6) and were located near but not on the reef (Fig. 1). The AR and NC sites were located in the same maritime space, experienced the same general environment, and differed only in proximity to the AR. Although our previous study (Chen et al., 2019) was also conducted in South China Sea, the study areas differed in the two studies. The previous study was conducted in the Pearl River Estuary, whereas the current study was conducted in southwest portion of Daya Bay, as noted above. As a consequence, the sampling sites differed between the two studies. 2.2. Investigation of macrobenthic fauna The macrobenthic fauna were sampled in April 2007, immediately before the AR was constructed, and for 2 years after AR construction, i.e., March 2008 (spring), May 2008 (summer), August 2008 (autumn), November 2008 (winter), and May 2009. On each of the six sampling dates, samples were simultaneously collected from both the AR and NC sites. On each sampling date, two sediment blocks were collected at each sampling site with a 0.1-m2 bottom grab sampler. The sediment was washed, stirred manually, and poured gently through a 1.0-mm sieve. The macrobenthic organisms retained on the sieve were stored in an alcohol solution (> 75% in volume ratio). The macrobenthic fauna were counted with the aid of a dissecting microscope and were identified to species with the help of standard taxonomic references (Huang and Lin, 2012). The macrobenthic faunal were dried to a constant weight in a vacuum oven, and the dry weight of each species was determined for calculation of eco-exergy. The specimens from two sediment blocks per site were pooled, leading to six replicates per habitat per sampling date. The procedures for sample collection and data analysis followed the standards specified for oceanographic-marine biological survey (China National Standardization Management Committee, 2007).
2. Materials and methods 2.3. Calculation of macrobenthic faunal taxonomic diversity indices
2.1. Study area and site
The following formulas were used to characterize the macrobenthic faunal communities at each site:
The study was conducted at the southwest portion of Daya Bay, inshore of Dapeng Peninsula, Shenzhen, South China (22o33′00′’22o34′08′’ N, 114o33′21′’-114o34′59′’ E, Fig. 1). Daya Bay is located in a subtropical region and covers 600 km2 in the Shenzhen marine area. The bay water depth ranges from 6 to 16 m. The mean annual air temperature is 22.0 °C, the mean coldest monthly temperature is 15.0 °C, the mean hottest monthly temperature is 33.5 °C, and the mean seawater surface temperature is 28.0 °C. The minimal sea surface temperature usually occurs in winter (average 17.3 °C), and the maximum occurs in summer (average 29.3 °C). Daya Bay contains diverse natural habitats, including coral reefs, mangrove forests, rock reefs, sandy shores, and mudflats (Wang et al., 2008). Since the 1980s, fishery resources in the Shenzhen marine area have
(1)
Dominance Y= (N i/ N )fi S
Shannon - Weiner diversity index H' = −
∑ (P i)(ln P i) i= 1
(2)
S
Simpson's diversity index D = 1 −
∑ (P i)2 i= 1
513
(3)
Simpson's diversity index D' = (S − 1)/lnN
(4)
Pielou's evenness index J′ = H′/lnS
(5)
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Fig. 1. Locations of the artificial reef (AR) and sampling sites in Daya Bay, China. There were six AR sampling sites, which were on the reef, and six non-reef control sites (NC), which were near but not on the reef.
where S is the total number of species, N is the total number of individuals, Ni is the number of species i, fi is the frequency of species i in each plot, and Pi is the proportion of individuals in a sample that belongs to species i. The Shannon-Weiner diversity index mainly contains information about the number and relative abundance of each species, and Simpson’s diversity index emphasizes the value of dominants in the community.
values used in this study were obtained from the Animal Genome Size Database (http://www.genomesize.com/index.php). For those species not in that database, their mean C-value for their genus, family, or order was used (Fu, 2015). The specific eco-exergy (SpEx) of the macrobenthic faunal community (i.e., the eco-exergy per unit mass of the fauna) was computed using Eq. (8):
2.4. Calculation of macrobenthic faunal eco-exergy and specific eco-exergy
⎛ SpEx = ⎜18.7 kj g−1 × ⎝
The eco-exergy (Ex) of the macrobenthic faunal community (i.e., the eco-exergy of the fauna per unit area) was computed using Eq. (6):
∑ Ci βi i=0
⎠
i=0
(8)
(6) 2.5. Statistical analysis
−1
is the mean energy of detritus or dead organic where 18.7 kj g matter; Ci is the biomass of the ith species (g m−2); and βi is the weighting factor of the genetic information based on Kullbach’s measurement of genome size and the complexity of the ith species (Jørgensen et al., 1995; Fu, 2015; Lu et al., 2015). Two methods (termed SWF and CV) are usually used to calculate the β value. In the SWF method, the suggested β value is used directly (Table S1; Jørgensen et al., 2005; Jørgensen and Nielsen, 2007). In the CV method, the β value is calculated from the C-value, which is the amount of DNA contained within a haploid nucleus as measured by the number of base pairs (bp) (Fu, 2015; Lu et al., 2015). In the CV method, the weighting factor β is calculated by Eq. (7):
β = 1 + ln(20165000000c)/7.43 × 105
i=0
where all factors and variables (Ci and βi) are defined in the same manner as those in Eq. (6).
n
Ex = 18.7 kj g−1 ×
n
n
∑ Ci βi⎞⎟/ ∑ Ci
One-way analysis of variance (ANOVA) was conducted to determine the differences in the taxonomic indices and eco-exergy of the macrobenthic faunal community among the six sampling periods in the same habitat. Independent-samples t tests were used to determine the differences in taxonomic indices and eco-exergy of macrobenthic faunal community between the two habitats. Paired-samples t tests were conducted to compare macrobenthic faunal eco-exergy and specific ecoexergy calculated by the SWF method vs. the CV method (ExSWF vs. ExCV and SpExSWF vs. SpExCV). Significance was set at p < 0.05. The similarities in the composition of macrobenthic faunal communities between the habitats were determined using the Bray-Curtis similarity coefficient for hierarchical cluster analysis (group-average linking method). Univariate regressions were used to analyze the relationships between the taxonomic indices and eco-exergy of the macrobenthic faunal community. The ANOVA, t tests, and regression analysis were performed using SPSS 20.0 (SPSS for Windows, IBM, Armonk, New York, USA). Cluster analysis was performed with Plymouth Routines in Multivariate Ecological Research (PRIMER) 4.5.
(7)
where 7.43 × 105 is the contribution of detritus to the eco-exergy in g/ l, and c is the total quantity of DNA in the diploid genome of the faunal cell in picograms (pg; 1 pg = 0.98 × 109 base pairs). Multiplication of c in pg by 1.65 × 108 is indicates the number of nucleotide triplets, which is the maximum coding capacity of the haploid genome given that each adjacent triplet of nucleotides corresponds to a transcribed RNA signal (Fonseca et al., 2000; Graur and Li, 2000). Twenty is the number of possible amino acids that can be coded by each triplet. All C514
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Fig. 2. Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, Pielou’s evenness, abundance, and biomass of the macrobenthic faunal community in AR and NC habitats in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Values are means ± SE. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
3. Results
after AR construction (Fig. 2E and F). Among the macrobenthic faunal groups, biomass and abundance were highest for mollusks regardless of habitat and sampling period. Biomass and abundance were often significantly lower for mollusks after AR construction (Table 2 and Table S2). Based on the Bray-Curtis similarity metric (group-average linking method) of macrobenthic faunal abundance data (fourth root, standardized), macrobenthic faunal assemblage patterns did not differ between the two kinds of habitats (Fig. 3). After AR construction, the diversity of dominant species in both habitats increased, and the dominance of mollusks gradually decreased (Table 1). In general, the macrobenthic taxonomic indices at any sampling time did not significantly differ between AR and NC habitats (Table S4).
3.1. Taxonomic diversity of the macrobenthic faunal community following artificial reef construction A total of 83 species and 1358 individuals of macrobenthic fauna, representing 7 phyla, 11 classes, and 55 families, were recorded from the two habitats (Table S2). The individuals belonged to seven phyla: mollusks (28 species), arthropods (9 species), annelids (36 species), Echinodermata (6 species), Chordata (2 species), Nemertea (1 species), and Echiura (1 species). These seven phyla represented 76.0, 1.7, 16, 3.7, 1.6, 0.5, and 0.5% of the 1358 individuals, respectively. Compared to the values obtained before the AR was constructed, the macrobenthic faunal Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, and Pielou’s evenness indices increased, but the abundance and biomass decreased after AR construction in AR habitats (p = 0.023, 0.050, 0.009, 0.048, 0.322, and 0.470, respectively) and in NC habitats (p = 0.001, 0.001, 0.006, 0.001, 0.012, and 0.137, respectively) (Fig. 2). All macrobenthic faunal taxonomic indices were frequently lower in winter than in other seasons of 2008 (Table S3; p = 0.023, 0.135, 0.062, 0.982, 0.335, and 0.014, respectively, compared with spring; p = 0.015, 0.115, 0.025, 0.270, 0.593, and 0.090, respectively, compared with summer; p = 0.041, 0.027, 0.033, 0.288, 0.957, and 0.247, respectively, compared with autumn). Values from the last sampling period (May 2009) suggested that macrobenthic faunal abundance and biomass might be beginning to increase 2 years
3.2. Eco-exergy of the macrobenthic faunal community following artificial reef construction Compared to their pre-construction values, macrobenthic faunal eco-exergy values (ExSWF and ExCV) decreased after AR construction in both AR and NC habitats (Fig. 4A and B); the values on the last sampling date, however, suggested that ExSWF and ExCV might be increasing in both kinds of habitats 2 years after the AR was constructed. Macrobenthic faunal specific eco-exergy (SpExSWF and SpExCV) decreased after AR construction in both AR and NC habitats. Among the macrobenthic faunal groups, ExSWF and ExCV were highest for mollusks regardless of habitat and sampling period. ExSWF and 515
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Fig. 3. Similarity of macrobenthic faunal communities in AR and NC habitats in Daya Bay, China as indicated by Bray-Curtis similarity coefficients. The habitat abbreviations are explained in Fig. 1. The numbers following the habitat abbreviations refer to the six sampling sites in each habitat. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
(Fig. 5E and F). Macrobenthic taxonomic and thermodynamic indicators were closely correlated to each other.
ExCV for mollusks, however, tended to decline after AR construction (Table 2). When compared for each sampling period, macrobenthic eco-exergy values (ExSWF and ExCV) and specific eco-exergy values (SpExSWF and SpExCV) were seldom significantly different between the two habitats (Table S4). Regardless of habitat or sampling period, macrobenthic faunal ExCV was significantly higher than macrobenthic faunal ExSWF (p < 0.001) (Table S5).
4. Discussion An integrated system of indicators can provide a comprehensive evaluation of ecological health and a reliable theoretical basis for environmental management (Tang et al., 2013, 2015, 2018). Veríssimo et al. (2017), for example, reported that both exergy-based and traitbased indices coherently reflected the effects of environmental change on benthic macroinvertebrates. Secondary production and eco-exergy were useful for revealing the response macrobenthic faunal assemblages to canopy cover conditions in Neotropical headwater streams, and suggested that thermodynamic indicators could help guide the decision making of environmental managers (Linares et al., 2018a).
3.3. Relationship between taxonomic indices and eco-exergy of the macrobenthic faunal community Based on linear regression, macrobenthic faunal Pielou’s evenness was negatively related to the ExSWF (Fig. 5D), and macrobenthic faunal abundance and biomass were positively related to ExSWF and ExCV
Table 1 The dominant species (Y > 0.01) in artificial reef and non-reef control habitats at each sampling time in Daya Bay, China. The April 2007 sampling time was before reef construction, and all other sampling times were after artificial reef construction. Sampling time
Artificial reef
Non-reef control
April 2007 March 2008 May 2008 August 2008 November 2008
Periglypta lacerata, Aglaophamus lyrochaeto, Capitella capitata Periglypta lacerata, Amphioplus laevis, Terebra dussumieri Lovenia subcarinata, Nothria holobranchiata, Amphioplus laevis, Aglaophamus lyrochaeto Glycera alba, Aglaophamus lyrochaeto, Sternaspis scutata Periglypta lacerata, Aglaophamus lyrochaeto, Aliculastrum cylindrica
May 2009
Babylonia areolata, Periglypta lacerata, Terebellides stroemii
Periglypta lacerata Periglypta lacerata, Lumbrineris heteropoda, Minolia chinensis Periglypta lacerata, Aglaophamus lyrochaeto, Lovenia subcarinata, Nothria holobranchiata Amphioplus laevis, Aglaophamus lyrochaeto, Glycera alba Aglaophamus lyrochaeto, Periglypta lacerata, Parachaeturichthys polynema, Turricula nelliae spurius Listriolobus brevirostris, Babylonia areolata, Aglaophamus lyrochaeto
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Table 2 The biomass and eco-exergy (values and proportions) of macrobenthic faunal groups at each sampling time in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Because biomass and eco-energy values did not significantly differ between reef sites (AR) and non-reef control sites (NC), values are averages across all sites. Sampling time
Category
Biomass(g m−2)
Proportion
ExSWF (kj m−2)
Proportion
ExCV (kj m−2)
Proportion
April 2007
Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total Arthropods Mollusks Annelids Echinodermata Nemertea Chordata Echiura Total
1.75 565.25 3.15 21.40 0.00 0.00 0.00 591.55 66.65 242.55 6.45 4.00 0.00 12.10 0.00 331.75 14.10 288.20 8.45 274.15 0.00 0.00 0.00 584.90 7.10 39.20 9.50 21.50 0.00 0.00 2.30 79.60 0.70 26.60 1.35 2.00 0.00 2.10 0.00 32.75 0.60 32.65 12.90 18.25 3.45 0.00 263.65 331.50
0.0030 0.9555 0.0053 0.0362 0.0000 0.0000 0.0000 1.0000 0.2009 0.7311 0.0194 0.0121 0.0000 0.0365 0.0000 1.0000 0.0241 0.4927 0.0144 0.4687 0.0000 0.0000 0.0000 1.0000 0.0892 0.4925 0.1193 0.2701 0.0000 0.0000 0.0289 1.0000 0.0214 0.8122 0.0412 0.0611 0.0000 0.0641 0.0000 1.0000 0.0018 0.0985 0.0389 0.0551 0.0104 0.0000 0.7953 1.0000
7.59E + 03 3.14E + 06 7.83E + 03 1.28E + 05 0.00E + 00 0.00E + 00 0.00E + 00 3.28E + 06 2.89E + 05 1.37E + 06 1.60E + 04 2.39E + 04 0.00E + 00 5.57E + 04 0.00E + 00 1.76E + 06 6.12E + 04 1.60E + 06 2.10E + 04 1.64E + 06 0.00E + 00 0.00E + 00 0.00E + 00 3.33E + 06 3.08E + 04 2.18E + 05 2.36E + 04 1.29E + 05 0.00E + 00 0.00E + 00 5.16E + 03 4.06E + 05 3.04E + 03 1.48E + 05 3.36E + 03 1.20E + 04 0.00E + 00 9.66E + 03 0.00E + 00 1.76E + 05 2.60E + 03 1.87E + 05 3.21E + 04 1.09E + 05 8.58E + 03 0.00E + 00 5.92E + 05 9.31E + 05
0.0023 0.9563 0.0024 0.0390 0.0000 0.0000 0.0000 1.0000 0.1647 0.7809 0.0091 0.0136 0.0000 0.0317 0.0000 1.0000 0.0184 0.4819 0.0063 0.4934 0.0000 0.0000 0.0000 1.0000 0.0759 0.5364 0.0582 0.3169 0.0000 0.0000 0.0127 1.0000 0.0172 0.8411 0.0190 0.0679 0.0000 0.0548 0.0000 1.0000 0.0028 0.2006 0.0345 0.1173 0.0092 0.0000 0.6356 1.0000
1.03E + 05 9.74E + 06 7.52E + 04 2.32E + 05 0.00E + 00 0.00E + 00 0.00E + 00 1.01E + 07 5.59E + 06 4.65E + 06 1.97E + 05 1.07E + 05 0.00E + 00 1.29E + 05 0.00E + 00 1.07E + 07 1.07E + 06 5.12E + 06 1.41E + 05 2.80E + 06 0.00E + 00 0.00E + 00 0.00E + 00 9.13E + 06 4.17E + 05 6.68E + 05 1.76E + 05 3.35E + 05 0.00E + 00 0.00E + 00 6.47E + 04 1.66E + 06 4.11E + 04 5.80E + 05 2.47E + 04 5.37E + 04 0.00E + 00 2.36E + 04 0.00E + 00 7.23E + 05 3.52E + 04 7.76E + 05 2.23E + 05 2.08E + 05 4.91E + 04 0.00E + 00 7.41E + 06 8.70E + 06
0.0101 0.9596 0.0074 0.0229 0.0000 0.0000 0.0000 1.0000 0.5237 0.4358 0.0184 0.0101 0.0000 0.0120 0.0000 1.0000 0.1173 0.5609 0.0154 0.3064 0.0000 0.0000 0.0000 1.0000 0.2512 0.4021 0.1060 0.2018 0.0000 0.0000 0.0389 1.0000 0.0568 0.8022 0.0341 0.0742 0.0000 0.0326 0.0000 1.0000 0.0040 0.0892 0.0256 0.0239 0.0056 0.0000 0.8516 1.0000
March 2008
May 2008
August 2008
November 2008
May 2009
habitats subjected to intermediate levels of disturbance (Townsend et al., 1997; Flöder and Sommer, 1999). ARs may also enrich the benthic assemblages in nearby habitats and contribute to increases in regional biodiversity (Carvalho et al., 2013; Chen et al., 2019). Macrobenthic abundance and biomass, however, might decrease soon after the disturbance of AR construction (Townsend et al., 1997; Flöder and Sommer, 1999). The effects of ARs on the benthic communities might change with AR age (Moura et al., 2007). Liu et al. (2017) found that the structure of the benthic macroinvertebrate community was stable in a 3-year period after AR construction and that benthic macroinvertebrate biomass increased with reef age. Long-term studies have confirmed that ARs frequently increase the abundance, species richness, and biomass of the benthic faunal community (Nicoletti et al., 2007; Brown et al., 2014). After initially declining following AR construction in the current study, macrobenthic faunal abundance and biomass also tended to increase at the end of the 2-year sampling period. In a previous study, AR construction increased benthic habitat heterogeneity, which led to increases in macrobenthic faunal species richness and diversity (Tews
Based on taxonomic and eco-exergy indicators, researchers have concluded that natural succession and invasion by the exotic cordgrass Spartina alterniflora threaten the macrobenthic faunal community in mangrove forests in Zhanjiang, China (Chen et al., 2018b,c). Before the current study, however, researchers had seldom explored the changes in integrated indicators of the macrobenthic faunal community following AR construction. In the present study, we found that the biodiversity, species richness, and evenness of the macrobenthic faunal community increased but that the abundance, biomass, and eco-exergy decreased on an AR and in the surrounding area during the 2-year period following AR construction. These results are different from those of a similar study conducted in the Pearl River Estuary, China, which found that macrobenthic faunal diversity, species richness, abundance, biomass, and ecoexergy all tended to increase after AR construction (Chen et al., 2019). These differences may result in part from the different locations used in the two studies. The results obtained with the AR in the current study are consistent with the intermediate disturbance hypothesis, which states that the taxon richness of macroinvertebrates will be highest in 517
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Fig. 4. Eco-exergy (ExSWF and ExCV) and specific eco-exergy (SpExSWF and SpExCV) of the macrobenthic faunal community in two habitats in Daya Bay, China. Habitat abbreviations are explained in Fig. 1. Values are means ± SE. 2007.04 = April 2007, 2008.03 = March 2008, 2008.05 = May 2008, 2008.08 = August 2008, 2008.11 = November 2008, and 2009.05 = May 2009.
faunal community following AR construction. Regardless of habitat or sampling period, the eco-exergies were significantly lower if calculated by the suggested β value (SWF) than by the C-value (CV). This mainly resulted from the different biological information included in the two calculations: the SWF method emphasizes species evolution level, and the CV method emphasizes species genetic information (Jørgensen, 2015; as indicated by the latter author, both methods have advantages). Whether based on the suggested β value or C-value, the eco-exergies of the macrobenthic faunal community significantly decreased during the 2-year period following AR construction in Daya Bay, China. We infer that AR construction at least initially reduced the stability and resilience of the macrobenthic faunal community.
et al., 2004). The hard texture of AR, however, makes it a difficult habitat for macrobenthic fauna, especially mollusks (Kristensen and Kostka, 2005; Wang et al., 2014). Considering that mollusks were the dominate group in term of abundance and biomass in the current study, the decrease in mollusk abundance and biomass led to the decline in the total abundance and biomass of the macrobenthic faunal community. Although the abundance and biomass of the macrobenthic faunal community tended to increase at the end of the 2-year sampling period following AR construction, determining whether these indices will continue to rise will require additional research. Like total abundance and biomass, the eco-exergy and specific ecoexergy of the macrobenthic faunal community declined during the first 2 years following AR construction in the present study. The changes in the dominant macrobenthic groups as the AR aged might help explain the changes in the eco-exergy of the macrobenthic faunal community. Although construction of the AR tended to increase the biomasses of non-mollusk macrobenthic groups in the current study, the construction significantly reduced mollusk biomass, and the suggested β value and Cvalue were higher for mollusks than for arthropods, annelids, and other groups (Table S1 and http://www.genomesize.com/index.php; Tang et al., 2013). As a consequence, the eco-exergy of the macrobenthic faunal community decreased during the 2-year sampling period of this study. That changes in macrobenthic faunal abundance were also positively related to changes in macrobenthic faunal eco-exergy in our study is reasonable because abundance and biomass are usually correlated for macrobenthic faunal communities (Baldrighi et al., 2014; Eklöf et al., 2017; Chen et al., 2019; and R2 = 0.58 and p = 0.004 in the current study) and because biomass is used in the calculation of ecoexergy. However, changes in macrobenthic faunal evenness were negatively related to changes in macrobenthic faunal eco-exergy, perhaps because macrobenthic species with larger body mass are more likely to have a random or aggregated distribution (Smith, 1980; Dolbeth et al., 2014; Vedenin et al., 2015). We also found a negative correlation between the biomass and evenness of the macrobenthic faunal community in the current study (R2 = 0.35, p = 0.042). We used two methods (SWF and CV) to measure the dynamics of the eco-exergies (eco-exergy and specific eco-exergy) of the macrobenthic
4.1. Limitations of this study Given that the effects of AR construction on macrobenthic fauna might change over time, a more complete understanding of the effects of ARs on marine organisms will clearly require long-term monitoring (Liu et al., 2017; Ng et al., 2017). Our understanding of AR effects would also benefit from the integrated assessment of additional macrobenthic faunal indicators, such as functional groups and production (Chen et al., 2018a; Linares et al., 2018a). Considering that the results of the current study differed from the results of our previous study (Chen et al., 2019), which was conducted in a different location in the South China Sea, future studies should include data from multiple study areas with and without AR construction. 5. Conclusions During the 2-year period following AR construction, the diversity, species richness, and evenness of the macrobenthic faunal community increased in both the AR habitat and the surrounding habitat, but the abundance, biomass, and eco-exergy of the community decreased; a possible increase in the latter properties was evident, however, in the last sampling period. Macrobenthic eco-exergy was closely correlated with abundance, biomass, and evenness but not with diversity or species richness. These results suggest that the use of multiple ecological 518
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Fig. 5. Linear relationships between ExSWF and ExCV and the Shannon-Weiner diversity, Simpson’s diversity, Margalef’s species richness, Pielou’s evenness, abundance, and biomass of the macrobenthic faunal community in Daya Bay, China. Equations, coefficients of determination, and probabilities are presented only when the linear regressions were significant (p < 0.05).
and with identification of the macrobenthic fauna. We also thank Dr. Zhifeng Wu at the Institute of Urban Environment, Chinese Academy of Sciences for help in making the map and Prof. Bruce Jaffee for editing the English grammar. This work was funded by the Central Public-interest Scientific Institution Basal Research Fund, South China Sea Fisheries Research Institute, CAFS (2017YB25) and the Shenzhen Science and Technology Innovation Project (JCYJ20160331141759795).
indicators (including eco-exergy and at least one measure of diversity) and long-term sampling in multiple research areas are needed to assess the effects of AR construction on the macrobenthic faunal community. Author contributions QC conceived the idea and designed the study; QC, HRY, and PMC carried out the field measurements, laboratory analyses, and statistical analyses, and contributed to manuscript writing and revisions. All authors contributed critically to the drafts and gave final approval for publication.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.01.049.
Acknowledgements We sincerely thank all the people who assisted with field sampling 519
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