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Article

Evaluating the Effects and Mechanisms of the Eco–Substrate in Aquaculture Environment Restoration from an Ecosystem Perspective via the Ecopath Model

by
Kai Zhang
†,
Junxian Jiang
†,
Zhifei Li
,
Ermeng Yu
,
Wangbao Gong
,
Yun Xia
,
Jingjing Tian
,
Hongyan Li
,
Wenping Xie
,
Jun Xie
* and
Guangjun Wang
*
Key Laboratory of Tropical and Subtropical Fishery Resource Application and Cultivation, Ministry of Agriculture, Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this study.
Sustainability 2024, 16(7), 2955; https://doi.org/10.3390/su16072955
Submission received: 2 March 2024 / Revised: 28 March 2024 / Accepted: 29 March 2024 / Published: 2 April 2024
(This article belongs to the Special Issue Towards Sustainable Agriculture: Pollution Prevention and Control Technologies)
Browse Figures
Versions Notes

Abstract

:
Aquaculture supplies high-quality and healthy proteins. With the increasing human demand for aquaculture production, intensive pond aquaculture developed rapidly and results in environmental deterioration. To solve this problem, the eco-substrate (ES), which is the biofilm carrier, has been utilized in aquaculture ponds. Studying the ecological mechanisms of ES from the perspective of the ecosystem may be conducive to the sustainable development of aquaculture. In this study, it was evaluated how ES makes a difference to the trophic structure, energy flow, and system characteristics of two different aquaculture pond ecosystems via the ecopath model. Three aquaculture ponds with ES were designed as the treatment ecosystem and three aquaculture ponds without ES were designed as the control ecosystem. There were 13 and 14 functional groups in the control and treatment ecosystems, respectively. The results showed that (1) the macrozooplankton and microzooplankton showed strong effects on the ecosystem in the keystoneness index; (2) energy transfer pathways in the treatment system with ES increased by 26.23% compared to the control system; (3) the ES improved the utilization rate of detritus, which was 14.91% higher than that of the control ecosystem; (4) the material and energy flow index and network information characteristics demonstrated the ES enhanced the complexity and stability of the treatment system. To improve the energy utilization efficiency, filter feeders can be introduced to ES ponds. Overall, the ES can alter the trophic structure, improve the energy utilization efficiency, and enhance the stability and maturity of aquaculture ecosystems, representing a sustainable practice. Considering the total area of aquaculture ponds on the earth reaching more than 5 million hectares, the application prospect of ES is broad.

1. Introduction

The production of aquaculture animals of the world reached 87.50 million tonnes in 2020 and provided high-quality and healthy animal proteins for humans [1]. Ponds are an important method of aquaculture. As demands for protein rose, the total pond areas for aquaculture worldwide increased remarkably to 5.4 million hectares (ha) [2]. Moreover, the intensive aquaculture ponds, which have a high density of cultured animals and require constant oxygenation and feeding, have become an important component [3]. Though the intensive pond aquaculture mode produced a large amount of aquaculture food and supplied animal protein effectively, there were still serious environmental concerns. Due to the low utilization of feed by cultured animals, the residuals are distributed into ponds, deteriorating the condition of the water and sediment in ponds [4]. In order to discard the pollution, water exchanging is usually performed after harvesting and leads to the deterioration the of surrounding environment [5]. Thus, finding a way to efficiently deal with the effluents is urgent.
Eco-substrate (ES) is the carrier of biofilm, which has a large specific surface area and volume utilization rate and can promote the enrichment of biofilm dominated by bacteria [6]. Biofilms drive several material cycling and energy flow pathways and thus, play important roles in environmental restoration [7]. ES has been installed in diverse ecosystems, including rivers [8], lakes [9], and reservoirs [10] and has wide functions in increasing hydraulic retention time, tuning the delivery of nutrients, mediating the fate of pollutants, which make great contributions to the self-purification of aquatic ecosystems [11]. To manage the environmental degradation issue caused by the intensive aquaculture, the ES has been utilized in aquaculture systems, including Litopenaeus vannamei aquaculture systems [12], Penaeus monodon aquaculture systems [13], and Oncorhynchus mykiss aquaculture systems [14], etc. The aquaculture ponds ecosystem is composed of different functional groups by ecological associations. Variation in one functional group may have an effect on other functional groups through ecological interactions, further affecting the entire ecosystem structure and function [15]. Since existing studies on this aspect mostly focused on the effect of ES on environmental factors, biological communities, and benefits [16], it is vital to value the role of ES from the perspective of ecosystem and provide new insights.
The ecopath, which is based on the ecosystem approach, is a mass-balanced trophic model. The ecopath model has been utilized in marines [17], lakes [18], rivers [19], and ponds ecosystems [20], including the interspecific relationships of the ecosystem [19], the effect of keystone species on the ecosystem, the trophic fluxes and energy transfers of ecosystems [20], the quantitative evaluation of anthropogenic stress on the ecosystem [21], the comparison of trophic structure and function of different ecological systems [22], and the health and maturity of the ecosystem [17,23]. However, few studies have focused on the ecosystem of aquaculture ponds with ES using ecopath model.
The largemouth bass (Micropterus salmoides), which is a native species of North America, has been cultured in Europe [24], Africa [25], and Asia [26] because of its fast growth rate, strong adaptability, and crucial economic significance. In China, the production of it kept increasing rapidly and has reached 300,000 tonnes [26]. Under this circumstance, the ecopath model was utilized for research the ecosystem characteristics, trophic structure, and energy fluxes of two different largemouth bass aquaculture pond ecosystems with and without ES. The objective was to evaluate the ecological effect of ES at the entire ecosystem level by comparing the structural and functional differences between two ecosystems. On this basis, the optimization measures of ES based on ecosystem level were put forward. This provided the basis for the management of sustainable aquaculture at ecosystem level.

2. Materials and Methods

2.1. Aquaculture System and Study Location

The experimental ponds are located at Huamiao Aquatic Food Co., Ltd., Sanshui District, Foshan City, Guangdong Province, China (112°55′08″ E, 23°26′46″ N, Figure 1). The experiment started on April 1 and ended on 16 November 2016. Attributed to the clear boundary of the pond ecosystem, the parameters of the ecopath model were relatively easy to determine and the results were reliable [27]. In pond ecosystem studies, three replicates were considered sufficient and the results were reliable. For example, Xiao et al. [28] studied the energy flow of the grass carp pond system based on the ecopath model. Two ponds of the same size were selected as the experimental objects, the pond area was 100 m × 50 m, the average water depth was 1.6 m, and the water area was 0.47 hm2. Aubin et al. [29] studied trophic webs using ecopath in six 500 m2 freshwater ponds with a mean depth of 0.8 m. Two replicates of 3 treatments were performed. In this study, two treatments were designed. Three aquaculture ponds (uniformed earthen ponds (160 × 65 × 2.5 m)) facilitated with ES were designed as the treatment ecosystem, while three aquaculture ponds of the same specification without ES were designed as the control ecosystem. The water depth of the pond was 2.0 m. During the experiment, the water was not changed and only the evaporated water was added.
This experiment utilized Aquamat ES (Meridian Aquatic Technology, L.L.C, Montana, USA), which had a specific surface area of 132.9 m2·m−2. As per the results of Zhang et al. [26] and Azim et al. [30], 25 Aquamats (2.0 × 1.5 m) equated with one hundred percent of the pond surface area.
To accelerate the formation of biofilm on the ES, all the ES were soaked for more than 10 min in the mixed carbon solution containing cellobiose, mannose, xylan, and galactose. The concentrations were 40, 20, 20, and 10 mg·L−1, respectively. The air drying method was applied to ES before being added to the ponds. The final thickness of ES was 0.05 m. The upper part of each ES was attached to a wire which ran diagonally across the pond and the base part was connected to rocks to make them fully unfold. The space between each two adjacent ES was 2.5 m.
The juvenile largemouth bass of this study was bought from the experimental location and stocked at 80,000 ind·ha−1. The initial weight was (12.3 ± 1.1) g and the initial body length (total body length to the fork of the tail) of the fish was (5.0 ± 0.4) cm. They were fed with iced fish twice a day (09:00 and 17:30). The feed rate was 3.0% to 5.0% of the body weight and adjusted according to the consumption of feed by largemouth bass. On sunny days, the aerators were opened from 13:00 to 15:00 and 18:00 to 06:00, otherwise, they were operated for the whole day.

2.2. Environmental Factors

A five-point sampling method was applied to collect water samples in each pond every 5 days. The YSI Professional Plus system (YSI Incorporated, Yellow Springs, OH, USA) was employed to measure water temperature, pH, and dissolved oxygen (DO) in situ. A TOC analyzer (Multi N/C 2100S, Analytik Jena AG, Jena, Germany) was used to determine the total organic carbon. The concentrations of total nitrogen and total phosphorus were determined using the potassium persulfate digestion method. The Griess-Saltzman method, the phenol disulfonic acid method, the salicylic acid spectrophotometric method, and the molybdenum blue method [24] were applied to determine nitrite nitrogen (NO2−-N), nitrate nitrogen (NO3−-N), ammonia nitrogen (TAN) and soluble reactive phosphorus (PO43−-P), respectively. All the methods were modified as by Laskov et al. [31] and Tu et al. [32].

2.3. Construction of Ecopath Models

The definition of the ecopath model is that the ecological system consists of a series of ecological functional groups, which can basically include the trophic levels and energy flow of the ecosystem. This study divided the control ecosystem into 13 functional groups, including the largemouth bass, macrobenthos (oligochaete, >500 um), microbenthos (oligochaete and nematodes, <500 um), macrozooplankton (copepods and cladocera, >150 um), microzooplankton (copepodites and rotifers, <150 um), bacterioplankton, benthic bacteria, micro-phytoplankton (>38 um), nano-phytoplankton (10–38 um), pico-phytoplankton (<10 um), iced fish, and detritus in the water and in the sediment, based on the biological characteristics and the species distribution in the pond ecosystem. The treatment ecosystem shared similar functional groups with the control ecosystem, with an additional group of periphyton added.
The ecopath model is based on two equations. The first equation describes the mass balance and the other one considers the energy balance.
The Equation (1) is expressed as in Christensen et al. [33]
B i   × ( P B ) i × E E i − ∑ j B j   × Q B j × D C j i − Y i   − B A i   − E i   = 0
where i is the prey group i and j is the predator group j. As for each functional group, B is biomass and P/B is the ratio of production to biomass. As for group i, EE is ecotrophic efficiency, which is the proportion of the production of group i that is utilized in the system. Y is the caught or harvested biomass, BA is the biomass accumulation, and E is the biomass of migration. As for group j, Q/B is the ratio of food consumption to biomass, DC is the diet composition and DCji means the ratio of prey group i in the food composition of predator group j.
The Equation (2) is expressed as:
B i × Q B i = B i × P B i + R i + U i  
where B, P/B and Q/B have the same meaning as those in Formula (1). R means the respiration and U means the food which cannot be assimilated by the consumers.
The Ecopath with Ecosim 6.5 software was used to construct the ecopath model. The diet composition (DC) is the necessary parameter. At least three of these four parameters (B, P/B, Q/B and EE) of each functional group were also necessary. The EE value is difficult to obtain, so the other three parameters are usually chosen to construct the ecopath model.

2.4. Input Data

In this study, biomass (B), P/B, Q/B and the diet composition were chosen to construct the ecopath model. All these data were the average value during the culture period.
Biomass: the biomass of the largemouth bass was the average value of initial and final body weight. Biomass of the other functional groups was calculated from the average value of monthly samples. Among them, the biomass of phytoplankton and zooplankton was measured using the volume-conversion method. The biomass of bacterioplankton and benthic bacteria was measured using the acridine orange fluorescence method. The periphyton was scraped from the eco-substrate and its biomass was determined with an electronic balance. The biomass of iced fish was determined with the average value of daily feeding weight. The other functional groups were directly sampled and determined with an electronic balance. The unit of biomass was t·km−2.
P/B ratio: the production of the largemouth bass was calculated as the difference between the initial and harvest weight. All the production of phytoplankton and periphyton groups was measured with the method of Diana et al. [34]. The production of macrozooplankton, microzooplankton, and benthic bacteria groups were measured according to the respiration and the production/consumption (P/Q) value obtained from Straile [35] and Moriarty [36]. The production of bacterioplankton was determined following the in situ culture method of Schwaerter et al. [37]. The P/B ratio of the above functional groups was determined according to the production and biomass. The P/B ratio of macrobenthos and microbenthos groups was obtained from Zhou et al. [20].
Q/B ratio: the Q/B value of largemouth bass was obtained from the FishBase website (www.fishbase.org, accessed on 1 March 2024) and determined with the method of Palomares and Pauly [38].
l o g Q B = 7.964 − 0.204 l o g W − 1965 T + 0.083 A
where W means the asymptotic weight of largemouth bass (g), T is the mean value of water temperature (K), A is the aspect ratio of largemouth bass.
The Q/B value of the macrozooplankton, microzooplankton, and bacterioplankton groups were determined by the respiration with the method of Williams [39]. The Q/B value of the benthic bacteria group was obtained by multiplying the sediment respiration by 0.64 [40]. The sediment respiration was measured by the sediment respirator [41]. The Q/B value of the macrobenthos and microbenthos groups were determined with the P/B value acquired from Schwinghamer [42] and P/Q value given by Lin et al. [43].
Diet composition: the diet composition is displayed in Tables S1 and S2. The diet composition of the largemouth bass, microzooplankton, macrozooplankton, microbenthos and macrobenthos were obtained with the carbon stable isotope method [44]. The diet composition of the benthic bacteria and bacterioplankton groups was obtained from the literature of Feng [22].
The unassimilated/consumption value of macrozooplankton and microzooplankton was 0.4 and attributed to their low digestive efficiency. Those of the other consumers was 0.2 [33].

2.5. Model Balancing

The biomass passed to the next trophic level was lower than that of the previous one, as a consequence, the EE value can never exceed 1.0. Therefore, the EE value < 1.0 was the primary criterion for the model balancing [45]. Additionally, the P/Q value ranging from 0.1 to 0.3 in each functional group was another criterion for the model balancing [45]. It was also considered that the respiration to assimilation ratio of each group was less than 1.0 and the respiration to biomass ratio (R/B) of active consumer groups was higher than sedentary consumer groups [33].

2.6. Ecological Indicators

The indexes involved in this study included ecotrophic efficiency (EE), keystoneness index, the total system throughput (TST), and transfer efficiency (TE), etc. The definition and calculation of these indicators followed the former study [46].

3. Results

3.1. Eco–Substrate Improving Water Quality

The fluctuation situation of water quality parameters is shown in Figure 2. There was no significant difference in pH, DO, water nitrite nitrogen, or soluble reactive phosphorus value between these two ecosystems (p > 0.05). The DO varied from 3.65 to 8.39 mg·L−1. Water pH fluctuated from 7.59 to 8.13. Water nitrite nitrogen ranged from 0.055 to 1.082 mg·L−1 and soluble reactive phosphorus fluctuated from 0.043 to 0.624 mg·L−1. Total organic carbon, nitrate nitrogen, ammonia nitrogen, total nitrogen, and total phosphorus in the treatment ecosystem were significantly lower than the control ecosystem (p < 0.05). The total organic carbon of the control ecosystem and the treatment ecosystem was 6.49 ± 0.39 and 5.68 ± 0.11 mg·L−1, respectively. The nitrate nitrogen of these two ecosystems was 3.01 ± 0.26 and 2.22 ± 0.07 mg·L−1. The ammonia nitrogen of these two ecosystems was 1.10 ± 0.13 and 0.59 ± 0.09 mg·L−1. The total nitrogen of these two ecosystems was 5.31 ± 0.13 and 3.79 ± 0.34 mg·L−1, respectively. As for the total phosphorus, the values were 0.75 ± 0.12 and 0.39 ± 0.05 mg·L−1, respectively.

3.2. Trophic Structure

3.2.1. Keystoneness Index

The keystoneness indexes of the two ecosystems are displayed in Table 1. The index of macrozooplankton group was the highest in the two ecosystems, followed by microzooplankton. The keystoneness indexes of largemouth bass and pico-phytoplankton of the ecosystem with ES was smaller than the control ecosystem, but the other groups showed the opposite tendency.

3.2.2. Food Web Structure

Results of the control and treatment systems’ food web structure are shown in Figure 3. The effective trophic level ranged from 1 to 2.65 and that of the microbenthos group was highest. Those of producers and detritus groups were closer to 1 and those of the consumer groups were closer to 2.
The results of the food chain in these systems are listed in Table 1. The largemouth bass had the biggest pathway numbers in the two ecosystems (24 and 31, respectively). Owing to the introduction of ES, the pathways of macrobenthos, microbenthos, macrozooplankton, and microzooplankton groups were improved in the treatment ecosystem. There were 61 and 77 total pathways number from primary producers and detritus groups to consumers in these two systems, respectively.

3.3. Energy Flow

3.3.1. Ecotrophic Efficiency

The output parameters of the model are shown in Table 2. The EE value of different functional groups ranged from 0.007 to 0.942. The macrozooplankton group showed the lowest EE value, among all the functional groups. The pico-phytoplankton and microbenthos group also obtained lower EE values compared to the other groups. It indicated that only a small amount of the biomass of these three groups was consumed by other groups. Most of the largemouth bass were harvested last, so the largemouth bass showed a high EE value. In the treatment group, EE value of the largemouth bass, macrobenthos, microzooplankton, iced fish, and detritus in water and sediment was higher compared to the control group.

3.3.2. Energy Consumption by Consumers

Figure 4 showed the total energy consumption by consumers in the two systems. In the control system, the consumption was ranked in the order of largemouth bass (31.95%) > benthic bacteria (31.60%) > macrozooplankton (14.54%) > microzooplankton (12.98%) > bacterioplankton (6.73%) > microbenthos (1.86%) > macrobenthos (0.34%). In the treatment system, it was ranked in the order of benthic bacteria (41.46%) > largemouth bass (35.64%) > microzooplankton (8.44%) > macrozooplankton (7.43%) > bacterioplankton (5.43%) > microbenthos (1.30%) > macrobenthos (0.29%). Thus, the greatest energy consumers in both the two ecosystems were the benthic bacteria and the largemouth bass.

3.3.3. Energy Flow between Trophic Levels

It is shown in Figure 5 the energy flow situation of different trophic levels in the two systems. The total production of primary producers in the control and treatment systems accounted for 21.75% and 17.01% of the total system throughput (TST), respectively. The energy from primary producers consumed by trophic level II was 5007 and 3114 t·km−2·year−1 in the control and treatment systems, accounting for 10.57% and 10.29% of the total primary production, respectively. The energy flow to detritus in water and sediment was 58,871 and 43,179 t·km−2·year−1 in the control and treatment systems, which was 60.64% and 60.19% of the TST, respectively. In the control and treatment ecosystems, 31,667 and 36,389 t·km−2·year−1 of detritus was utilized by trophic level II, revealing 14.91% more for the treatment ecosystem when compared with the control ecosystem. Generally, the majority of energy was centered at trophic level I and energy decreased significantly with the increase in trophic level.

3.4. System Characteristics

System characteristics of the two systems are shown in Table 3. Compared to the treatment group, it was with higher value for TST, total primary production, net system production, total production, total exports and total flow to detritus, and lower values for total consumption, total respiration, and total biomass (excluding detritus) in the control group. Additionally, the control ecosystem exhibited higher total primary production/total biomass (TPP/TB), Finn’s mean path length (FML) and total primary production/total respiration (TPP/TR) as compared with the treatment ecosystem, whereas the Finn’s cycling index (FCI) and connectance index (CI) were lower for the control ecosystem.

4. Discussion

4.1. Eco–Substrate Alter Trophic Structure

4.1.1. Keystoneness Index of the Two Ecosystems

The highest keystoneness index was obtained from macrozooplankton in this study. Macrozooplankton in the aquaculture ecosystems feeds mainly on phytoplankton and microzooplankton, showing a top-down effect on the ecosystem. Additionally, macrozooplankton was also one of the food sources for the fish and showed a bottom-up effect. Results showed that the macrozooplankton group is an important link in material transferring between different trophic levels, given its position in the food web (Figure 4).

4.1.2. Food Web of the Two Ecosystems

The structure analysis of aquaculture ponds ecosystem via the ecopath model can supply deep understanding of the ecosystem attributes and lay a foundation for the energy flow analysis, thus providing a reference for the optimization of the aquaculture pond ecosystem. All the trophic levels were lower than three (Figure 3) in this study. This result accorded with some other studies. Dong et al. found the trophic level of the rice-crayfish ecosystem was less than three [47]. Feng et al. achieved the same result in the polyculture system of Portunus trituberculatus [22]. Longer food chains mean more energy loss [48], so aquaculture systems use the method of shortening the food chain to mitigate the loss of energy input, which leads to the low trophic level.
Ecosystems consist of many pathways through which matter and energy are transferred from low to high trophic levels [49]. System connectivity is indicated based on the quantity of pathways [50]. The more pathways there are, the richer the connections [46]. Matter and energy of the ecosystem can be transferred by other pathways when suffering disturbances. This contributes to strengthen the anti-interference capability of ecosystems, which helps to maintain ecosystem function. In this study, the food chain analysis showed the pathways number of the treatment ecosystem was improved by 26.23% more than the control ecosystem (Table 1), exhibiting a more “web-like” configuration. It indicated the ES could improve the stability of aquaculture pond ecosystems.

4.2. Energy Utilization Efficiency Improved by Eco-Substrate

In this study, the largemouth bass was a large consumer of energy (Figure 4). Since the largemouth bass derives food mainly from the iced fish, the energy flow from the iced fish to largemouth bass is an important energy pathway in the two ecosystems. Additionally, the benthic bacteria, which used detritus in sediment as a main energy source [51], also consumes a high proportion of the total energy consumption (Figure 4). Therefore, the benthic bacteria form the main channel for energy from the sediment detritus to enter the ecosystem network for energy flowing. Nonetheless, the EE values of benthic bacteria in these two ecosystems were both close to 0.1, indicating that little energy was transferred from benthic bacteria to the higher consumer’s trophic levels. As for the mechanism, the low biomass of microbenthos, the main predator of bacteria, did not favor the energy passed from bacteria to the higher trophic level. Furthermore, the respiration of bacteria consumes enormous energy [41]. Both of these reasons impede energy transfer from detritus to high trophic levels. The bacteria play a decomposer role in the two ecosystems and break down the organic material that other consumers utilized with difficultly. Therefore, in the two ecosystems, the bacteria maintain the stability of ecosystem mainly through the “decomposer” role, instead of being food for consumers.
The addition of ES improved the EE value of some groups. The mechanism has been mentioned in our former study [26]. The ES can offer shelter and cover [52], broaden the surface area for microorganisms to attach to [16], decrease water ammonia nitrogen, and inhibit opportunistic pathogens in the water and the fish gut [24]. All these reasons bring the higher survival rate and output of the fish, and the higher feed utilization efficiency in the treatment system than the control ecosystem. Therefore, the EE value of largemouth bass and iced fish groups has been improved.
The detritus utilized by trophic level II of the treatment ecosystem was 14.91% more than that of the control ecosystem. The EE value of detritus in water and sediment groups also showed a similar tendency. The ES improved the production of largemouth bass (Table S3), which partly feed on macrobenthos and macrozooplankton (Tables S1 and S2), and promoted the growth of microbenthos and microzooplankton groups indirectly. Microbenthos and microzooplankton groups are the main predators of bacteria. Therefore, the ES improved the transfer energy effectively from detritus to trophic level II in the treatment ecosystem. Additionally, the detritus in the sediment also supplied part of the food for the largemouth bass (Tables S1 and S2). It also led to more energy transferred from detritus to trophic level II in the treatment ecosystem.

4.3. Stability and Maturity Enhanced by Eco-Substrate

The ecopath model provides various indicators to describe the ecosystem characteristics. These indicators can be used to compare functions of ecosystems. The Finn cycling index (FCI) represents the ratio of matter or energy flowing into detritus that is recycled in the system to the TST [53]. Increasing FCI value in ecosystems means the rise of utilization efficiency of internal matter or energy and the decline of dependence on external substances or energy, which represents improvements of ecosystem stability [54]. Due to the predatory behavior of the largemouth bass, macrobenthos, macrozooplankton and microzooplankton groups on periphyton in the ES, more matter or energy entered into detritus was reutilized in the treatment ecosystem, which improved the FCI of the treatment system. This indicated that ES could strengthen the internal cycle of the system and enhance the system stability by utilizing energy effectively.
As mentioned in Part 4.1, the energy transfer pathways number indicated connectivity of the system [50]. According to Table 1, the pathway number of the treatment system was improved by 26.23% more than the control system (Table 1) and was more “web-like” than the control system (Figure 3). It indicated the ES could make the ecosystem more complex and stable through strengthening the potential for system resilience.
Moreover, there are some other indicators to describe the ecosystem characteristics. TPP/TR and TPP/TB ratios are indexes to describe development stage and vitality of ecosystems [53]. These ratios close to 1.0 indicate more system maturity [33]. CI and SOI are indicators that characterize the nutritional relationships of ecosystems and food web structure [33]. Generally, a more mature system with greater stability is reflected by larger values of CI and SOI [33,55,56,57]. There was same SOI value for these two systems in this study, but the TPP/TR and TPP/TB values of the treatment system with ES were closer to 1 than those of the control system and the treatment system showed higher CI than the control system. These results indicated that ES can enhance the maturity of pond ecosystems, which accords with system stability. As for the mechanism, it could be attributed to the improvement of ES to the water environment [58]. Although the ES affects the ecosystem mainly through the effect on the inorganic environment, the organisms on the ES provide part of the food source for other groups in the system (Table 2), which expands the paths and diversity of the system’s energy flow (Figure 3, Table 1 and Table 3). Therefore, the treatment ecosystem may foster greater maturity and stability, which has a stronger ability to resist external disturbance, such as from disease and the environment changing.

4.4. Further Optimization of the Aquaculture Ecosystem

In this study, the energy from primary producers flow into the detritus groups reached 89.43% and 89.71% of the total primary production for the control and treatment ecosystems, respectively (Figure 5). This result was in accordance with the low EE value of phytoplankton groups (Table 2). This indicated a potential for further utilization of phytoplankton in the ecosystem. Introducing and utilizing filter feeders, such as silver carp (Hypophthalmichthys molitrix), bighead carp (Aristichthys nobilis), and bivalves, which feed effectively on phytoplankton [38], can improve the utilization of phytoplankton, increase total energy exports, and reduce detritus in the ecosystem, thereby preventing harm to the aquaculture environment caused by excessive deposits of detritus in the sediment [20]. Selecting suitable filter feeders and matching them with ES to more effectively regulate the environment of aquaculture ponds will be the next key work.

5. Conclusions

This study utilized the ecopath model to describe the trophic structure and energy flow of the ES ponds. The results showed that the ES can change the system trophic structure by increasing the pathways number in the food web, enhance the food chain energy flow and thus, improve the transfer efficiency. The ES was proved to be a sustainable practice because of its function on improving energy utilization efficiently and by enhancing the maturity and stability of ecosystem. However, there were also some problems such as the low utilization efficiency of the phytoplankton groups and the simple trophic structure. It is necessary to stock filter feeders in the ES ponds to improve the energy utilization efficiency and the aquaculture structure and proportion could be further optimized on the basis of the ecopath model. Considering the total area of aquaculture ponds on Earth reaches more than 5 million hectares, the ES has great potential to maximize aquaculture returns and sustainability.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16072955/s1, Table S1: Diet matrix of the control ecosystem, the proportion of each prey group in the predator’s diet is indicated by the displayed values. Table S2: Diet matrix of the treatment ecosystem, the proportion of each prey group in the predator’s diet is indicated by the displayed values. Table S3: Harvest information of cultured animals in these two ecosystems.

Author Contributions

Conceptualization, K.Z., J.X. and G.W.; Data curation, K.Z. and J.J.; Formal analysis, K.Z.; Investigation, K.Z., J.J., Z.L., E.Y., W.G. and Y.X.; Methodology, K.Z.; Software, K.Z.; Visualization, J.T.; Supervision, H.L.; Validation, W.X.; Project administration, J.X.; Funding acquisition, G.W.; Resources, J.X. and G.W.; Writing—original draft preparation, K.Z.; Writing—review & editing, K.Z., J.X. and G.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (42077453), the National Key Research and Development Program (2019YFD0900302; 2023YFD2400504), the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-45-21), and the Central Public-interest Scientific Institution Basal Research Fund (CAFS2023TD62).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on request from the authors.

Acknowledgments

The authors also thank Wang Jinlin, Li Jiansong, Zhang Zhiqiang and Wu Jingrong for their help during this study. We thank Feng Jie of the Institute of Oceanology of the Chinese Academy of Sciences for his suggestions on this paper.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Map of sampling fields in Sanshui County, Foshan City, Guangdong Province, China.
Figure 1. Map of sampling fields in Sanshui County, Foshan City, Guangdong Province, China.
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Figure 2. Dynamics of environment factors in different groups. X-axis is the duration of experiment.
Figure 2. Dynamics of environment factors in different groups. X-axis is the duration of experiment.
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Figure 3. Food web structure of the two ecosystems (the thickness of the curve is according to the quantity of material flow. The size of circles is proportional to the biomass on a logarithmic scale. Trophic levels are denoted in the left side).
Figure 3. Food web structure of the two ecosystems (the thickness of the curve is according to the quantity of material flow. The size of circles is proportional to the biomass on a logarithmic scale. Trophic levels are denoted in the left side).
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Figure 4. The proportion of energy consumption to total energy consumption of each consumer in the control and treatment ecosystems.
Figure 4. The proportion of energy consumption to total energy consumption of each consumer in the control and treatment ecosystems.
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Figure 5. The energy flow among different trophic levels in the control and treatment ecosystems (P: primary production; D: detritus; TL: trophic level; TE: transfer efficiency; TST: total system throughput).
Figure 5. The energy flow among different trophic levels in the control and treatment ecosystems (P: primary production; D: detritus; TL: trophic level; TE: transfer efficiency; TST: total system throughput).
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Table 1. The keystoneness index of the control and treatment ecosystems.
Table 1. The keystoneness index of the control and treatment ecosystems.
GroupThe Keystoneness IndexThe Total Number of Pathways
The Control EcosystemThe Treatment EcosystemThe Control EcosystemThe Treatment Ecosystem
1The largemouth bass−0.678−0.8842431
2Macrobenthos−0.145−0.128811
3Microbenthos−0.376−0.36546
4Macrozooplankton−0.0151−0.01171417
5Microzooplankton−0.0645−0.024878
6Bacterioplankton−1.016−0.82422
7Benthic bacteria−0.439−0.41522
8Micro-phytoplankton−0.782−0.766
9Nano-phytoplankton−0.783−0.770
10Pico-phytoplankton−0.704−0.809
11Periphyton——−1.874
Total value 6177
Table 2. Basic input and calculate parameters for the ecopath model of the two pond ecosystems.
Table 2. Basic input and calculate parameters for the ecopath model of the two pond ecosystems.
Group NameBiomass (t·km−2)P/BQ/BEEDetritus Import (t·km−2)
CTCTCTCTCT
1The largemouth bass2043.002412.502.072.075.935.930.9330.942
2Macrobenthos6.105.506.446.4423.5923.590.3080.404
3Microbenthos23.3017.309.239.2331.3531.350.0600.041
4Macrozooplankton30.5321.3853.2750.27183.61141.830.0070.013
5Microzooplankton7.805.10197.69206.18641.73675.850.2730.216
6Bacterioplankton13.599.3265.7187.12190.81237.840.8450.649
7Benthic bacteria51.2858.7873.9772.83237.61287.920.1270.071
8Micro-phytoplankton35.3026.70147.32127.51 0.3180.302
9Nano-phytoplankton70.7853.30158.73181.2 0.1430.106
10Pico-phytoplankton106.9278.92289.45215.19 0.0560.057
11Periphyton——8.20——27.24—— ——0.378
12Iced fish44.2449.88 0.8160.82716,145.6918,208.35
13Detritus in water2668.002335.17 0.0890.125
14Detritus in sediment6152.006306.00 0.2540.416
C: the control system, T: the treatment system. Values estimated via the ecopath model are shown in bold.
Table 3. Comparison of system characteristics of the control and treatment ecosystems.
Table 3. Comparison of system characteristics of the control and treatment ecosystems.
ParametersThe Control EcosystemThe Treatment EcosystemUnits
Total system throughput (TST)217,865.30177,996.50t·km−2·year−1
Total consumption (TC)38,378.5840,597.99t·km−2·year−1
Total exports47,286.3329,702.60t·km−2·year−1
Total respiration (TR)16,242.6618,774.39t·km−2·year−1
Total flow to detritus115,957.7088,921.47t·km−2·year−1
Total production59,721.2842,676.81t·km−2·year−1
Total primary production (TPP)47,383.3030,268.64t·km−2·year−1
Net system production31,140.6411,494.25t·km−2·year−1
Total biomass (excluding detritus) TB2388.602697.00t·km−2
TPP/TR2.921.61
TPP/TB19.8411.22
Connectance index (CI)0.280.29
System omnivory index (SOI)0.050.05
Finn’s cycling index (FCI, %)5.057.64
Finn’s mean path length (FML)3.433.67
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Zhang, K.; Jiang, J.; Li, Z.; Yu, E.; Gong, W.; Xia, Y.; Tian, J.; Li, H.; Xie, W.; Xie, J.; et al. Evaluating the Effects and Mechanisms of the Eco–Substrate in Aquaculture Environment Restoration from an Ecosystem Perspective via the Ecopath Model. Sustainability 2024, 16, 2955. https://doi.org/10.3390/su16072955

AMA Style

Zhang K, Jiang J, Li Z, Yu E, Gong W, Xia Y, Tian J, Li H, Xie W, Xie J, et al. Evaluating the Effects and Mechanisms of the Eco–Substrate in Aquaculture Environment Restoration from an Ecosystem Perspective via the Ecopath Model. Sustainability. 2024; 16(7):2955. https://doi.org/10.3390/su16072955

Chicago/Turabian Style

Zhang, Kai, Junxian Jiang, Zhifei Li, Ermeng Yu, Wangbao Gong, Yun Xia, Jingjing Tian, Hongyan Li, Wenping Xie, Jun Xie, and et al. 2024. "Evaluating the Effects and Mechanisms of the Eco–Substrate in Aquaculture Environment Restoration from an Ecosystem Perspective via the Ecopath Model" Sustainability 16, no. 7: 2955. https://doi.org/10.3390/su16072955

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