Ecosystem Characteristics and Trophic Model of the Artificial Reef Ecosystem in the Sea of Oman, Sultanate of Oman

Abstract

This study aimed to understand the structure and function of the artificial reef ecosystem of the Sea of Oman and its stability and maturity. For this study, the trophic model of the Sea of Oman’s artificial reef ecosystem was described using the Ecopath with Ecosim (EwE) ecosystem modeling software (Version 6.6.7). The essential characteristics of the aquatic system were identified using a total of 38 fish species/functional groups, spread across an area of 140 km² of artificial reef farm. The mean trophic level of the artificial reef ecosystem of the Sea of Oman was 3.039. Sharks were the keystone species of the studied ecosystem. Heniochus acuminatus and Chaetodon gardneri were the species with the highest niche overlap, whereas Acanthurus sohal and other crustaceans, and Terapon puta and Saurida undosquamis were the species with the lowest niche overlap. It was found that the ratio of total primary production to total respiration of the ecosystem studied was more than one, indicating that the system produces more energy than it uses to respire, and the ecosystem of the Sea of Oman can be regarded as a developing system because of its low degree of stability and maturity. The omnivory index was 0.260, the connectance index was 0.159, the total biomass to total throughput ratio was 0.006, Finn’s cycling index was 5.41, the total primary production to total biomass ratio was 64.895, and the total primary production to total respiration ratio was 4.424. The results indicate that the artificial reef ecosystem in the Sea of Oman can be categorized as immature (in the early developmental stage). Further study is needed to improve the input data and track ecosystem health, as well as exploring other management strategies. Based on the outcomes of the study, it is suggested that environmental management of the reef ecosystem, along with the fish catch data, should be taken into consideration for future research.

1. Introduction

Coral reefs are one of the most productive and ecologically diversified fragile ecosystems in the world, due to human activity and natural occurrences like storms and climate change [1]. Artificial reefs (ARs) are a potential tool for ecosystem restoration after damage where a physical structure is needed (e.g., by providing habitat or a barrier) [2]. ARs are man-made structures constructed underwater to duplicate the role of the natural reefs; the materials used in artificial reefs provide the same ecological functions as real habitats, developing epibiotic communities that generate microhabitats for motile species [3]. In addition to preventing the degradation of ecosystems, ARs are used to maximize fish assemblage and create new habitats, increasing fish production and stock [4,5,6,7,8]. New habitats such as artificial reefs can benefit fisheries by redistributing biomass or providing breeding opportunities [9]. The deployment of ARs as a resource can limit the decline of fisheries and/or promote their recovery [10]. The demand for new resources will increase, due to the global reduction in marine fisheries and global population growth by 2 billion by 2050 [10]. The available literature suggests that ARs contribute to protecting the surrounding area by renewing and increasing the populations of various reef-associated organisms and different marine resources [11,12]. Kasim [13] suggested that new ARs should be created for many purposes, for example as barriers (as in Spain, France, and Italy), to increase the biomass of the benthic biota in the ocean, or to offer stable surfaces for coral to settle on, as they are otherwise harmed by coal mining [2]. ARs have received variable degrees of acceptance from over 50 nations for their role in ecosystem restoration and the production of fisheries [14]. In the Gulf countries, ARs date back over a century, to when fishermen first placed date palm trunks, stones, pottery, and other materials in coastal areas to improve their catch [15]. Contemporary ARs have taken different shapes, structures and roles [16]. For example, the Marawah Marine Biosphere Reserve in Abu Dhabi seeks to use ARs to attract commercially important fish, while in southern Iran, ARs are aimed at attracting lobsters [16].

As a part of projects undertaken by the Ministry of Agriculture, Fisheries and Water Resources (MAFWR) since 2003, approximately 18,325 AR units have been deployed around the Omani coast, at a total cost of OMR 4,093,107 (approximately USD 10,600,000). Exactly 14,410 units of ARs have been deployed so far in the Al-Batinah governorate in the Sultanate of Oman (Figure 1). The MAFWR’s primary objective with these projects was to enhance the productivity of fisheries in Al-Batinah. The largest AR farm in the Middle East is in Al-Batinahin Wilayat A’Suwaiq (with a length of 20 km, a width of 7 km, and a depth of 15–30 m) [17]. However, the ecological effectiveness of these projects has not been evaluated. Salaün et al. [18] pointed out that this absence of feedback creates concerns about the actual ecological function of these AR platforms.

Quantitative trophic web models, which are based on the measurement of energy and matter fluxes in ecosystems, may be used for this purpose, since they describe interactions between species at various trophic levels. Ecopath with Ecosim (EwE) is one such model. It provides a quantitative snapshot, or representation, of the ecosystem under study in terms of trophic fluxes and biomasses [19]. Several studies using Ecopath have been conducted. They investigated how AR ecosystems were being operationalized and how ARs impacted the system’s trophic interaction and production, with most of them being based in China [20]. For example, an Ecopath model was built to study the energy flow pattern and the system features of the ecosystem based on the yearly inquiry data of biological resources in the Lidao artificial reef zone in the Rongcheng area of Shandong Province, East China, in 2009 [20]. Similarly, the ecosystem framework and functioning of the nearshore ARs in the Lidao coastal ecosystem of northern China were studied using the Ecopath with Ecosim model [21]. In addition, Ecopath modeling was employed to determine the ecosystem state of the ARs in contrast to the system’s status before the AR deployment in the eastern part of Laizhou Bay, in the Bohai Sea in China [22]. Furthermore, a pair of trophic models (Ecopath) were developed at an AR area and at an adjacent natural reef on the north Yellow Sea on the China coast, to investigate possible variations in the functioning of the ecosystem for both types of reef ecosystem [23].

Besides the studies using the AR Ecopath model in China, additional investigations were conducted in a few other countries. The modeling method was also used to investigate how ecological groups interacted with the fishing impact in the northern Sea of Oman [24]. Furthermore, the coastal fisheries ecosystem of the northern Persian Gulf’s trophic model was described by Taghavimotlagh et al. [25], using Ecopath modeling. This current research will look at the trophic relationships, energy fluxes, keystone species, and ecological features to see how mature the ecosystem is and how artificial reefs may have affected the system’s trophic interaction and production. Following the involvement of the ARs, the ecological observations are examined, using food-web modeling, with which they answer the crucial question—did the adoption of ARs contribute to the maturing ecosystem? Moreover, the identification and comparison of energy and matter flow at AR sites in the research system are reported, together with keystone species, energy fluxes, ecosystem parameters, and fishing impacts.

2. Materials and Methods

2.1. Study Area

This study was conducted on the 140 km² area of an artificial reef farm in the Sea of Oman in the Sultanate of Oman (Figure 1). The studied region has the highest number of fishermen and fishing vessels in the Sultanate of Oman, accounting for 26.6% of the artisanal fishery [26]. The MAFWR has launched the AR projects in the Sea of Oman to increase the productivity of fisheries. The studied region has the largest AR farm in the Middle East.

2.2. An Overview of the Model (Model Building and Functional Groups)

The Ecopath with Ecosim (Version 6.6.7) modeling software was used to assess the artificial reef ecosystem structure in the Sea of Oman, and to perform an ecosystem trophic analysis [27]. Two linear equations were used to parameterize the model and to represent for each species or functional group the energy balance between production and losses. Biomass fluxes within groups are mass-balanced throughout the model development process, to make sure that a group’s consumption equals its production, growth, and respiration losses.

Thus, the production for each group can be expressed as follows:

$$B_i\times {\left(\raisebox{1ex}{$P$}\!\left/ \!\raisebox{-1ex}{$B$}\right.\right)}_i\times {EE}_i=Y_i+\sum B_i\times {\left(\raisebox{1ex}{$Q$}\!\left/ \!\raisebox{-1ex}{$B$}\right.\right)}_j\times {DC}_{ji}$$

(1)

where B_i is the biomass of the prey group i; P/B_i is the ratio of the group i’s production to its biomass; EE_i is the ecotrophic efficiency; Y_i is the yield (fishery catch); B_j is the biomass of the predator group j; Q/B_j is the food intake-per-unit biomass of j; and DC_ji is the proportion of i in j’s diet. The input data were normalized to represent biomass (B) as wet weight (t/km²) and production/biomass (P/B) and consumption/biomass (Q/B) rates annually, for each functional group (t/km²/Year). The ectotrophic efficiency (EE) has no unit [28,29,30].

For this study, fish species/functional groups were collected from December 2021 to November 2022. Five samplings were conducted during this period. Four basket-shaped fence traps were set up for each sample and kept in place for five consecutive days. The traps had a base that was 2 m in diameter, a height of 1.2 m, and a mesh size of 4 cm (Figure 2). The weight and length of each species was recorded onsite, to estimate the fish biomass, along with different population parameters. Over 3400 individual fishes were found within the 20 traps used on the 25 sampling days (Figure 3). Alutera monoceros was the only fish species found in all the samples. During sampling, the traps were disturbed across five locations within the 140 km² AR farm. The traps were set at the following coordinates: 23°54.8813 N, 57°24.3619 E; 23°54.4373 N, 57°23.4680 E; 23°54.0787 N, 57°30.4605 E; 23°55.3280 N, 57°22.2904 E, and 23°55.0713 N, 57°27.1170 E.

2.3. Basic Parameterization

2.3.1. Biomass (B_i)

Biomass data of functional groups, except for phytoplankton, zooplankton and detritus, were collected from the fence basket traps. The total biomass was collected by multiplying the total number of fishes found in the traps by the average weight of the fishes sampled and by the total fence trap area (a standard trap was used). All the units were converted to t/km². The phytoplankton and zooplankton biomass were taken from the study of [24] and divided by the total artificial reef farm area in km². The detritus biomass was collected from the study conducted by Tajzadeh Namin et al. [24] (

Table 1. Input data of the Ecopath model built on the Artificial Reef ecosystem of the Sea of Oman, Sultanate of Oman, considering different functional groups.

Sl No	Functional Group	Bi (t/km2)	P/B (/Year)	Q/B (/Year)	Shape
1	Sharks	8 × 10−2	1.45	6.22
2	Rays	3 × 10−1	0.75	5.96
3	Stephanolepis diaspros	2.37 × 10−4	1.88	27.3
4	Argyrops spinifer	2.38 × 10−3	0.85	13.4
5	Velifer hyposelopterus	1.83 × 10−5
6	Nemipterus bipunctatus	1.86 × 10−1	1.72	10.8
7	Terapon puta	3.92 × 10−3	1.69	16
8	Decapterus russelli		1.44	11.9
9	Pristipomides filamentosus	2.44 × 10−3	0.811	6.5
10	Heniochus acuminatus	5.97 × 10−4	2	9.5
11	Lutjanus bengalensis	2.66 × 10−4	2.28	11
12	Acanthurus xanthopterus	5.13 × 10−3	2.45	20.2
13	Lutjanus ehrenbergii	6.53 × 10−3	0.51	11.4
14	Lutjanus sanguineus	9.18 ×10−3	0.96	5.9
15	Alutera monoceros	9.18 × 10−3	1.11	6.5
16	Diodontidae		2.01	11
17	Antennarius indicus		4.14	28.5
18	Acanthurus sohal	2.40 × 10−4	2.8	25.8
19	Saurida undosquamis	5.60 × 10−3	1.15	11.8
20	Parupeneus macronema	4.31 × 10−3	2.76	8.4
21	Lutjanus madras	3.63 × 10−3	0.89	24.2
22	Scolopsis vosmeri	2.23 × 10−3	0.59	10.5
23	Chaetodon gardneri	7.99 × 10−3	1.73	34.1
24	Scolopsis ghanam	9.72 × 10−3	3.09	11
25	Sphyraena flavicauda		0.71	10.8
26	Sardinella longiceps
27	Rastrelliger kanagurta
28	Pomadasys stridens		1.14	13.3
30	Cephalopholis aurantia	3.02 × 10−3	1.46	7.3
31	Chaetodon nigropunctaus	4.07 × 10−4	3.14	14.1
32	Coral	0.56
33	Invertebrates	0.68	5.63	19.2
34	Crustaceans	2.20	6.41	42.77
35	Benthos	2.27	5	178
36	Zooplankton	34.4	52
37	Phytoplankton	110.29	110
38	Detritus	2.511

Bi = Initial biomass (t/km²); P/B = Production/Biomass (/year); Q/B = Consumption/Biomass (/year).

).

2.3.2. Production/Biomass (P/B)

The von Bertalanffy Growth Function (VBGF), which states that estimations of total mortality (Z) may be utilized as input values in fish populations when individuals are growing, can be used to compute the production-over-biomass ratio (P/B) in Ecopath models. Therefore, Z is equal to P/B [31] (see Appendix A). Using data from the literature and FishBase, the P/B for the species most representative of each functional category was calculated [32] (Table 1).

2.3.3. Consumption/Biomass (Q/B)

Consumption is the amount of food consumed by a functional group during a certain period, and it is usually expressed as a ratio of consumption to biomass (Q/B) in the Ecopath model. The metric ‘t/km²/year’ is used to convey absolute consumption, as calculated by Ecopath; however, because the Q/B is a ratio, it is unitless. The following empirical relationship can be used to determine the value of Q/B for any fish species:

$$log\raisebox{1ex}{$Q$}\!\left/ \!\raisebox{-1ex}{$B=7.964-0.204\log W\infty -1.965T+0.083Ar+0.532h+0.398d$}\right.$$

where W∞ is the asymptotic weight (g), T is an expression for the mean annual temperature of the water body, expressed using T = 1000/Kelvin (Kelvin = °C + 273.15), Ar is the metabolic activity ratio of the square of the caudal fin’s height to its surface area, h is a dummy variable that equals 1 for herbivores, 0 for detritivores and carnivores, and d is also a dummy variable, which is equal to 1 for detritivores and 0 for herbivores and carnivores [33]. The Q/B for this study has been calculated from the data available from the literature and FishBase [32] (Table 1).

2.3.4. Diet Composition (DC)

Information on the diets of various species and groups was gathered from the available literature and FishBase [32] (see Appendix B). According to Pauly et al. [34], diet composition is used to estimate the trophic levels. Therefore, a diet matrix of the functional groups was built by designating the proportion of each prey that each predator consumed (see Appendix C).

2.3.5. Ecotrophic Efficiency (EE)

The ecotrophic efficiency (EE) of a system is a measurement of how much of its output is utilized, transferred up the food chain, or used for biomass buildup, migration, or export [35]. Direct evaluation of ecotrophic efficiency is a challenging task. It ranges from 0 to 1, and is predicted to become closer to 1 for populations under heavy predation pressure. In this study, the EE was calculated using the Ecopath with Ecosim (EwE) ecosystem modeling software (Version 6.6.7).

2.4. Model Balancing and Uncertainty

Most Ecopath models are built using a set of input parameters composed of regional means for the selected period. After the Ecopath parameters are loaded into the program and the underlying assumptions are verified using the PREBAL approach, the model must be balanced to maintain the laws of thermodynamics (Figure 4). A well-balanced model has an EE value under 1. Otherwise, it suggests that there is a large demand for the group, and changes are made to the diet’s composition and biomass (mean or maximum biomass), until a balanced model is reached [29] (Heymans et al., 2016).

The EE value for phytoplankton and detritus is lower than that of fish groups, which is typically greater [36]. In addition, the ratio of production to consumption (P/Q), also known as the gross food conversion efficiency, has a value between 0.05 and 0.3 [37]. Along with investigating incompatible vital rates (P/B, Q/B, etc.), the Ecopath software (Version 6.6.7) determines the EE value that supports the validation of models.

2.5. Model Analyses and Ecological Indicators

In Ecopath, there are several system indicators that objectively stand for ecosystem stability and degree of system maturity. Total system throughput (TST), which is the sum of all four ecosystem flows, including total consumption, total exports, total respiration and energy going to the detritus, is an illustration of one of these representative measures [37]. The total-primary-production to total-respiration (TPP/TR) ratio is another metric that may be employed to evaluate the maturity of an ecosystem [38,39]. According to Christensen and Walter [37], the ecosystem is more mature when the TPP/TR ratios are closer to 1. The Finn’s Cycling Index (FCI) is the proportion of the total amount of nutrients entering the system to be recycled again. In more established and reliable systems, higher amounts of recycling are frequently observed [39]. The connectance index (CI) and the system omnivory index (SOI) are two indicators of how intricate the interactions are within the food web. The values of CI and SOI for more established ecosystems are close to 1 [37]. The trophic level (TL) analysis is the ecological status of the functional groupings in the model. The Mixed Trophic Impact (MTI) approach was used to investigate direct and indirect impacts between those functional groups, through an extensive examination of the food web [27].

3. Result

3.1. Trophic Structure

There were five trophic levels and 38 species/functional groups in the artificial reef ecosystem’s food-web structure in the Sea of Oman (Figure 5). Sharks were at trophic level 4.349, whereas primary producers (i.e., phytoplankton and detritus) were at trophic level 1. Sharks, rays, Stephanolepis diaspros, Velifer hyposelopterus, Acanthurus xanthopterus, Diodontidae and Cephalopholis aurantia had the lowest EE values, which were equal to zero. The highest values of ecotrophic efficiency were for benthopelagic fish such as Pristipomides filamentosus (0.998), the reef-associated fishes such as Chaetodon gardneri (0.983), Chaetodon nigropunctaus (0.966), Heniochus acuminatus (0.927), Scolopsis vosmer (0.908), and benthos (0.939) (

Table 2. The modified input and output parameters of the Ecopath model of the Artificial Reef ecosystem of the Sea of Oman, Sultanate of Oman.

	Group Name	Trophic Level	Biomass (t/km2)	Production/Biomass (/Year)	Consumption/Biomass (/Year)	Ecotrophic Efficiency	Production/Consumption (/Year)	Flow to Detritus (t/km2/Year)	Net Efficiency	Omnivory Index
1	Sharks	4.349	0.0008	0.015	6.220	0.000	0.002	0.000	0.002	0.176
2	Rays	3.698	0.0003	0.075	5.960	0.000	0.013	0.000	0.016	0.201
3	Stephanolepis diaspros	3.554	0.0024	1.880	27.300	0.000	0.069	0.017	0.086	0.146
4	Argyrops spinifer	3.754	0.0002	0.850	13.400	0.732	0.063	0.001	0.079	0.329
5	Velifer hyposelopterus	2.827	0.0002	1.120	20.100	0.000	0.056	0.001	0.070	0.518
6	Nemipterus bipunctatus	3.373	0.1864	1.720	10.800	0.002	0.159	0.723	0.199	0.087
7	Terapon puta	2.904	0.0392	1.690	16.000	0.464	0.106	0.161	0.132	0.365
8	Decapterus russelli	2.605	0.0127	1.440	11.900	0.600	0.121	0.038	0.151	0.367
9	Pristipomides filamentosus	3.554	0.0078	0.881	6.500	0.998	0.136	0.010	0.169	0.146
10	Heniochus acuminatus	3.282	0.0089	2.460	9.500	0.927	0.259	0.019	0.324	0.300
11	Lutjanus bengalensis	3.627	0.0047	2.280	11.000	0.710	0.207	0.013	0.259	0.227
12	Acanthurus xanthopterus	3.105	0.0050	2.450	20.200	0.000	0.121	0.032	0.152	0.379
13	Lutjanus ehrenbergii	3.753	0.0085	1.100	11.400	0.945	0.096	0.020	0.121	0.189
14	Lutjanus sanguineus	3.962	0.0092	0.960	5.900	0.056	0.163	0.019	0.203	0.336
15	Alutera monoceros	2.840	0.0012	1.110	6.500	0.262	0.171	0.003	0.213	0.304
16	Diodontidae	3.495	0.0023	2.010	11.000	0.000	0.183	0.010	0.228	0.121
17	Antennarius indicus	3.353	0.0029	4.140	28.500	0.800	0.145	0.019	0.182	0.085
18	Acanthurus sohal	2.235	0.0064	2.800	25.800	0.838	0.109	0.036	0.136	0.221
19	Saurida undosquamis	3.891	0.0056	1.150	11.800	0.843	0.097	0.014	0.122	0.273
20	Parupeneus macronema	3.271	0.0004	2.760	9.300	0.210	0.297	0.002	0.371	0.315
21	Lutjanus madras	3.278	0.0063	1.880	24.200	0.579	0.078	0.035	0.097	0.402
22	Scolopsis vosmeri	3.108	0.0052	1.150	10.500	0.908	0.110	0.011	0.137	0.438
23	Chaetodon gardneri	3.319	0.0080	2.270	34.100	0.983	0.067	0.055	0.083	0.309
24	Scolopsis ghanam	3.622	0.0097	3.090	11.000	0.008	0.281	0.051	0.351	0.167
25	Sphyraena flavicauda	3.232	0.0054	1.710	10.800	0.027	0.158	0.021	0.198	0.261
26	Sardinella longiceps	2.494	0.0200	4.600	47.600	0.509	0.097	0.236	0.121	0.366
27	Rastrelliger kanagurta	2.606	0.0060	3.400	11.600	0.573	0.293	0.023	0.366	0.369
28	Pomadasys stridens	3.151	0.0109	1.140	13.300	0.700	0.086	0.033	0.107	0.531
29	Cephalopholis aurantia	3.327	0.0003	1.460	7.300	0.000	0.200	0.001	0.250	0.689
30	Chaetodon nigropunctaus	3.273	0.0085	3.140	14.100	0.966	0.223	0.025	0.278	0.405
31	Coral	2.358	0.1000	6.500	22.600	0.582	0.288	0.723	0.360	0.300
32	Invertebrates	2.247	7.8000	9.630	32.200	0.867	0.299	60.210	0.374	0.244
33	Carpilius convexus	2.609	2.2000	6.410	42.770	0.042	0.150	32.327	0.187	0.372
34	Other Crustaceans	3.081	1.5000	6.600	40.000	0.048	0.165	21.421	0.206	0.134
35	Benthos	2.111	30.2700	23.000	70.000	0.939	0.329	466.567	0.411	0.111
36	Zooplankton	2.235	34.4000	52.000	120.000	0.265	0.433	2140.756	0.542	0.221
37	Phytoplankton	1.000	110.2900	110.000		0.324		8203.915		0.000
38	Detritus	1.000	2.5110			0.141		0.000	0.000	0.284

Values marked in Bold are calculated using the EwE software (Version 6.6.7).

). The P/Q ratio of the functional groups varied from 0.002 (sharks) to 0.433 (zooplankton). The EE value for phytoplankton was 0.324, showing that the system only uses a slight part of the production. The findings of this study clearly show that all groups of fishes, except sharks and rays, are exploited (Table 2).

3.2. Transfer Efficiency of the Model

The Sea of Oman’s artificial reef ecosystem consisted of five main aggregated trophic levels (Figure 6). Almost all the energy was transferred between trophic levels one and two. A total of 12,132 t/km²/year of primary production was produced by ecosystem primary producers. Consumers ingested around 3928 t/km²/year, which entered trophic level two, and 8204 t/km²/year, which entered the detrital pool from primary producers. The amount of energy entering trophic levels two, three, four, and five was 18.79%, 1.885%, 0.066%, and 0.00546%, respectively, of the ecosystem’s overall flow. The overall transfer efficiency of the studied artificial reef ecosystem of the Sea of Oman was 6.623%. Moreover, the primary producer was found to transmit 6.476% of the energy, whereas the detritus transmitted 6.970% (

Table 3. Transfer efficiency (calculated as geometric mean for TL II–IV) of different trophic levels of the Ecopath model of the Artificial Reef ecosystem of the Sea of Oman, Sultanate of Oman.

Source\Trophic Level	II	III	IV	V	VI	VII	VIII	IX
Producer	9.527	3.687	7.732	1.079	4.337
Detritus	11.32	3.097	9.655	1	4.253
All flows	10.03	3.5	8.272	1.053	4.31	4.622	3.032	2.495
Proportion of total flow originating from detritus: 0.43
Transfer efficiencies (calculated as geometric mean for TL II–IV)
From primary producers: 6.476%
From detritus: 6.970%
Total: 6.623%

).

3.3. Mixed Trophic Impact Analysis

The mixed trophic impact (MTI) analysis of the artificial reef’s ecosystem was used to demonstrate how different trophic levels and changes in biomass in one group affect the others (Figure 7). The results showed that detritus and phytoplankton had a positive impact on all factional groups within the system. Zooplankton impacted themselves negatively, due to cannibalism; this is known as the zero-order cycle. They also had a negative effect on phytoplankton, as it is a source of food for them. Moreover, they have a negative effect on detritus, as well. Sharks have negative effects on themselves and on the benthos pelagic species such as Terapon puta, Decapterus russelli, and Pristipomides filamentous, and positive effects on the pelagic species such as Sardinella longiceps and Rastrelliger kanagurta. Based on the MTI matrix, the impact of most of the reef-associated species is unclear within the ecosystem.

3.4. Niche Overlap Analysis

The overlap of niches of functional groupings was partially represented by prey-and- predator overlap indices (Figure 8). Heniochus acuminatus and Chaetodon gardneri were the species with the highest niche overlap, whereas Acanthurus sohal and other crustaceans, as well as Terapon puta and Saurida undosquamis, were the species with the lowest niche overlap. Decapterus russelli and Rastrelliger kanagurta had the greatest prey-overlap index among all the functional groups. On the other hand, Nemipterus bipunctatus, Argyrops spinifer and Alutera monoceros, had the greatest predator-overlap index.

3.5. Respiration and Assimilation

The respiration and assimilation of several functional groups within an artificial reef ecosystem are shown in

Table 4. Respiration and assimilation of the functional groups of the Artificial Reef ecosystem of the Sea of Oman, Sultanate of Oman.

Group Name		Respiration (t/km2/Year)	Assimilation (t/km2/Year)	Respiration/Assimilation	Production/Respiration	Respiration/Biomass (/Year)
1	Sharks	0.005	0.005	0.998	0.002	6.206
2	Rays	0.001	0.001	0.984	0.016	4.693
3	Stephanolepis diaspros	0.047	0.052	0.914	0.094	19.960
4	Argyrops spinifer	0.002	0.003	0.921	0.086	9.870
5	Velifer hyposelopterus	0.003	0.003	0.930	0.075	14.960
6	Nemipterus bipunctatus	1.290	1.610	0.801	0.249	6.920
7	Terapon puta	0.436	0.502	0.868	0.152	11.110
8	Decapterus russelli	0.103	0.121	0.849	0.178	8.080
9	Pristipomides filamentosus	0.034	0.041	0.831	0.204	4.319
10	Heniochus acuminatus	0.046	0.068	0.676	0.479	5.140
11	Lutjanus bengalensis	0.031	0.041	0.741	0.350	6.520
12	Acanthurus xanthopterus	0.069	0.081	0.848	0.179	13.710
13	Lutjanus ehrenbergii	0.068	0.078	0.879	0.137	8.020
14	Lutjanus sanguineus	0.035	0.043	0.797	0.255	3.760
15	Alutera monoceros	0.005	0.006	0.787	0.271	4.090
16	Diodontidae	0.016	0.020	0.772	0.296	6.790
17	Antennarius indicus	0.055	0.067	0.818	0.222	18.660
18	Acanthurus sohal	0.114	0.132	0.864	0.157	17.840
19	Saurida undosquamis	0.046	0.053	0.878	0.139	8.290
20	Parupeneus macronema	0.002	0.003	0.629	0.590	4.680
21	Lutjanus madras	0.110	0.122	0.903	0.108	17.480
22	Scolopsis vosmeri	0.038	0.044	0.863	0.159	7.250
23	Chaetodon gardneri	0.200	0.218	0.917	0.091	25.010
24	Scolopsis ghanam	0.055	0.085	0.649	0.541	5.710
25	Sphyraena flavicauda	0.038	0.047	0.802	0.247	6.930
26	Sardinella longiceps	0.670	0.762	0.879	0.137	33.480
27	Rastrelliger kanagurta	0.035	0.056	0.634	0.578	5.880
28	Pomadasys stridens	0.104	0.116	0.893	0.120	9.500
29	Cephalopholis aurantia	0.001	0.002	0.750	0.333	4.380
30	Chaetodon nigropunctaus	0.069	0.096	0.722	0.386	8.140
31	Coral	1.158	1.808	0.640	0.561	11.580
32	Invertebrates	125.814	200.928	0.626	0.597	16.130
33	Carpilius convexus	61.173	75.275	0.813	0.231	27.806
34	Other crustaceans	38.100	48.000	0.794	0.260	25.400
35	Benthos	998.910	1695.120	0.589	0.697	33.000
36	Zooplankton	1513.600	3302.400	0.458	1.182	44.000

. Zooplankton had high rates of respiration (R) (1513.600 t/km²/year) and assimilation (A) (3302.400 t/km²/year). Among other functional categories, invertebrates had high R and A values (125.814 and 200.928 t/km²/year, respectively) as did Carpilius convexus (61.173 and 75.275 t/km²/year, respectively) and other crustaceans (38.100 and 48.000 t/km²/year, respectively). Higher trophic levels always have low R and A values.

As assimilation cannot be more than respiration, the ratio of the two cannot ever be greater than 1. For top predators whose production is relatively low, the respiration/assimilation ratio should be close to 1. The ratio will generally be lower, but still positive, for species at lower trophic levels. The group activity is represented by the R/B ratio, which stands for respiration/biomass. The ratio increases in direct proportion to the degree of activity, for a certain group. The greatest R/B ratios were found in benthos and zooplankton. In fact, zooplankton had the greatest R/B ratio (44.000), while Lutjanus sanguineus had the lowest R/B ratio (3.760) (Table 4).

3.6. Total System Statistics and Model Comparison

The results of the study on the Sea of Oman ecosystem that are evident from the Ecopath with Ecosim suite’s total system statistics are displayed in

Table 5. System statistics of the Ecopath model of the Artificial Reef ecosystem of the Sea of Oman, Sultanate of Oman.

Parameter	Value	Units
Sum of all consumption	6660.009	t/km2/year
Sum of all exports	9389.419	t/km2/year
Sum of all respiratory flows	2742.481	t/km2/year
Sum of all flows into detritus	10,927.550	t/km2/year
Total system throughput	29,719.460	t/km2/year
Sum of all production	14,717.430	t/km2/year
Mean Trophic level	3.039
Calculated total net primary production	12,131.900	t/km2/year
Total primary production/total respiration	4.424	-
Net system production	9389.419	t/km2/year
Total primary production/total biomass	64.895	-
Total biomass/total throughput	0.006	t/km2/year
Total biomass (excluding detritus)	186.946	t/km2
Connectance Index	0.159
System Omnivory Index	0.260
Ecopath pedigree	0.171
Measure of fit, t	1.028
Shannon diversity index	1.162
Throughput cycled (excluding detritus)	624.7	t/km2/year
Predatory cycling index	7.943	% of throughput without detritus
Throughput cycled (including detritus)	1608	t/km2/year
Finn’s cycling index	5.41	% of total throughput
Finn’s mean path length	2.45	none
Finn’s straight-through path length	2.64	without detritus
Finn’s straight-through path length	2.317	with detritus

, along with the overall system statistics of the artificial reef ecosystem. The total system statistics include the sums of each flow inside the system. The total system throughput is 29,719.460 t/km²/year, the total system consumption is 6660.009 t/km²/year, and the total system flow into detritus is 10,927.550 t/km²/year. A total of 9389.419 t/km²/year is the net system production. The system throughput is a crucial metric. It is the sum of four flows—total consumption, total export, total respiration, and total flow-to-detritus (Figure 9). The largest throughput percentage is in the detritus flow (37%), followed by exports (32%) and consumption (22%). The total biomass/total system throughput ratio was 0.006, the TPP/TR ratio was 4.424, and the TPP/total biomass ratio was 64.895. The FCI and the FML in the artificial reef ecosystem was 5.41 and 2.45, respectively. The CI and SOI values were 0.159 and 0.260, respectively (Table 5).

3.7. Keystone Index

Sharks were the key functional group in the Sea of Oman artificial reef ecosystem (KS = 0.321). Lutjanus sanguineus, Saurida undosquamis, and Lutjanus ehrenbergii had keystone indices of 0.178, 0.0807, and 0.0221, respectively, among the higher-trophic-level species. The keystone indices of Diodontidae, Cephalopholis aurantia and Velifer hyposelopterus at the lowest trophic levels were −1.818, −2.751, and −2.922, respectively. Sharks had the greatest relative total effect (RTI) on the Sea of Oman ecosystem (1.0), followed by Lutjanus sanguineus (0.721), Saurida undosquamis (0.576), and phytoplankton (0.523) (Figure 10).

4. Discussion

The tropical level (TL) has developed as an essential quantitative indicator for ecosystem study, since it appears to be an explanatory variable for many factors in ecological and fishery contexts [40]. The TL of different groups of the artificial reef ecosystems is displayed in Table 2. Sharks fall into the category of animals that have a TL which is greater than 4. Rays, some demersal species such as Stephanolepis diaspros and Argyrops spinifer, some benthos pelagic fish such as Pristipomides filamentosus, and most of the reef-associated fish such as Lutjanus bengalensis, Lutjanus ehrenbergii, Lutjanus sanguineus, Saurida undosquamis and Scolopsis ghanam have a TL of more than 3.5. The mean trophic level (MTL) of the Sea of Oman artificial reef ecosystem is estimated to be 3.039, which is almost the same as the Arabian Sea off the coast of Karnataka [41]. It is also relatively close to the MTLs of the Northern Sea of Oman [24] and the northern Persian Gulf, both of which have an MTL of 3.49 [25]. However, it is higher than the MTL of the Bay of Bengal and the Sundarbans estuary, which have an MTL of 2.716 [36].

In exploited marine ecosystems, the MTL of fisheries landings is typically used as a sustainability measure. Because of this, fisheries initially tend to remove large, slowly developing fish, which decreases the MTL of the fish that are still present in an ecosystem [42]. However, under the sequential collapse/replacement paradigm, a decline in the MTL should be accompanied by a decline in the capture of high-trophic-level species, as these species reach economic exhaustion [43]. The system’s low MTL demonstrates the relative absence of top predators [36]. The P/Q ratios varied from 0.002 to 0.433 for the various species in the study. Christensen and Walters [37] state that the P/Q values should usually fall between 0.05 and 0.3; however, in the current investigation, zooplankton had a high value of 0.433, as the zooplankton is a fast-growing functional group (Table 2).

Transfer efficiency is the percentage of total fluxes at one trophic level that are consumed by another trophic level. Estimated transfer efficiencies are used to organize ecosystem components into distinct levels [44]. According to Odum [45], the range of TE in coastal ecosystems is between 10 and 20%. The TE for this investigation is 6.623%, which indicates that the studied artificial reef ecosystem of the Sea of Oman had not matured. The TE is calculated using the geometric mean of trophic levels 2–4. As a result, it demonstrates that the detrital food route was dominant in this production environment. In this investigation, 0.43 of the overall flow fractions came from the detritus (Table 3).

In fact, detritus accounted for 43.0% of the overall energy flow in the ecosystem, and was mostly distributed throughout the different trophic levels of the ecosystem chain, while primary producers made up 57.0%. As a result, the ecosystem had low transfer efficiency, since many primary producers entered the food chain and were rapidly consumed by predators at higher trophic levels (Table 3). The third trophic level’s biomass comprised 60.5% of the overall biomass, and practically all its species fed on lower levels, resulting in a poor utilization rate for detritus. This, in turn, had a significant impact on the present ecosystem.

A comparison of our model’s functional attributes with several existing marine ecosystem models is found in

Table 6. Comparison of system attributes of the Sea of Oman artificial reef ecosystem model with other ecosystems.

Ecological Indicator	TST	TPP/TR	CI	SOI	FCI	FML
Current Study	29,719.46	4.424	0.159	0.26	5.41	2.45
Artificial Reef Ecosystem in Laizhou Bay [46]	17,300.57	1.205	0.207	0.09	76.15	16.62
Laizhou Bay (2016) [21]	4697.28	0.52	0.208	0.164	22.52	4.09
ARs and a nearby NR on the north Yellow Sea, China [23]	6455.47	1.93	0.3	0.2	-	-
Lidao artificial reef, China [22]	11,104	1.82	0.32	0.14	5.46	-
The northern Persian Gulf [25]	245,310.70	29.92	0.15	0.29	0.63	-
Northern Oman Sea [24]	27,581.70	3.57	0.44	0.42	5.7	2.27
Bay of Bengal, Sundarbans Estuary [47]	4163.712	1.445	0.269	0.276	14.39	3.203

TST = Total system throughput; TPP/TR = Total primary production/total respiration; CI = Connectance Index; SOI = System Omnivory Index; FCI = Finn’s cycling index; FML = Finn’s mean path length.

. The ecosystem’s current stability and maturity are objectively reflected by a variety of indicators included in Ecopath. Total system throughput (TST), which represents the entire scale of ecosystem fluxes, is one example of a representative indicator. Other examples are total consumption, total exports, total respiration, and total energy going to detritus. During this study, it was found that the TST of the ecological parameter of artificial reefs in the Sea of Oman was 29,719.46. This was extremely high, but nearly the same as the TST of the Northern Sea of Oman [24]. However, this is in sharp contrast to the TST of other artificial ecosystems in the Laizhou Bay of China, as found during studies in 2022 (17,300.57) and in 2016 (4697.28) [21,46], and the TST of ARs and a nearby NR in the north Yellow Sea, China (6455.47) [23] and the Lido artificial reef [22]. As Odum [38] stated, the ratio of total primary production (TPP) over total respiration (TR) is one of the 24 indicators employed to assess the maturity of an ecosystem. The TPP/TR ratio is regarded as a key sign of the maturity of an ecosystem. When this ratio is closer to 1, the ecosystem is more stable, and able to endure external interruption. This study found that the TPP/TR ratio of the artificial reef ecosystem was 4.424 (Table 6). This means that the ecosystem is still in the developing stage, considering that the reef’s development had only been completed recently, late in 2020. As the TPP/TR ratio was higher than 1, it meant that this was an immature ecosystem. This contrasts with the TPP/TR of the AR ecosystem of Laizhou Bay [46], which was more advanced and stable, since it was established in 2011 (Table 6).

The value comparison of FCI for the current study was estimated to be 5.41, indicating that the ecosystem recycled 5.41% of its energy, which is almost the same as the FCI of the Lidao artificial reef in China [22] and the Northern Sea of Oman [24]. The low FCI of the artificial ecosystem in the Sea of Oman indicates that the ecosystem is under stress. The FCI of the artificial reef ecosystem in Laizhou Bay [46] was relatively high, possibly indicating that the ecosystem there has a higher production, as compared to the one in the Sea of Oman which was studied during this research.

In this study, the CI and SOI of the ecology of the artificial reef in the Sea of Oman were 0.159 and 0.260, respectively. The CI value was almost identical to that found in the northern Persian Gulf [25], while the SOI value was nearly identical to that found in the Sundarbans estuary in the Bay of Bengal [47] (Table 6). The results were considerably less than 1, indicating that the food web structure was not particularly complex, and that the ecosystem’s functional groupings were not directly linked [48].

In conclusion, this study’s analysis of the characteristics of several ecosystems revealed that the artificial reef ecosystem of the Sea of Oman can be regarded as a developing system because of its low degree of stability and maturity. This ecosystem has a high ratio of total primary production to total respiration (>1), which shows that the system produces more energy than it uses to respire. Also, it has a low biomass-to-throughput ratio, CI value, and SOI value (<1). The transfer efficiency is below normal, and it has a high total-primary-production to total-biomass ratio, which indicates that the system has only very little biomass accumulating.

Further research will be necessary to enhance the input data, monitor the health of the ecosystem, and investigate other management options that might highlight the advantages of ecosystem-based management. Since the locations of the artificial reefs have not been revealed to the public, and the MAFWR has not gathered any catch data, no catch data were available for the current study. Based on the study’s findings, it is recommended that environmental management of the reef ecosystem and fish catch statistics be considered. To manage fisheries sustainably and according to ecological standards, it is also advisable to determine each component of the food web’s biomass, feeding ecology, and population dynamics. The Ecopath with Ecosim software (Version 6.6.7) is a great way to accomplish this goal, since it allows us to evaluate how ecological groups interact with one another and how fishing activities affect the ecosystem of the artificial reefs in the Sea of Oman, both of which are essential for creating effective management plans. The efficacy of this ecosystem can be improved using efficient ecosystem-based fisheries management practices.