A Concept and Framework of the Extended Ecosystem-Based Fisheries Assessment Approach Incorporating Other Driving Forces

Abstract

The ecosystem-based fisheries assessment (EBFA) approach to evaluate four management objectives: sustainability, biodiversity, habitat quality, and socio-economic benefits, has been developed in previous studies. The existing EBFA approach is a risk-based assessment framework and was designed to assess the impacts of fisheries on offshore ecosystems. This approach only considers one driving force of wild capture fisheries. However, in coastal ecosystems, there are a number of anthropogenic activities. In this study, we propose an extended EBFA approach that incorporates the effects of capture fisheries and other driving forces, including various human activities and natural processes. This paper focuses on (i) revising the process and equations related to the nested risk indices defined in the existing EBFA approach, and (ii) demonstrating the applicability of the proposed approach by applying it to Uljin coastal waters and comparing the results with the previous case study of the existing EBFA. However, indicators and their relevant reference points have not yet been fully developed—particularly for the tier 1 approach. Hence, further research, especially regarding the reference points, would be required for practical use of the proposed approach.

1. Introduction

There are major shortcomings in single species management as we can see from the cases of such management, which have led to overfishing in many areas. Single species management is very limited and by focusing only on sustainability and ignoring habitat quality and ecological interactions [1,2].

The world is striving to maintain the sustainability of marine food resources. The FAO stressed the implementation of an ecosystem approach to fisheries (EAF) [3]. In addition, the World Summit on Sustainable Development (WSSD) encouraged the application of an ecosystem-based approach of fisheries by 2010 [4]. The Future We Want, the report released by the United Nations Conference on Sustainable Development (UNCSD), stipulates norms for the conservation of marine ecosystems and the efficient management of fishery resources [5].

In 2016, more than 160 countries around the world adopted the 2030 Sustainable Development Goals (SDGs), and the 14th goal of ’conserve and sustainably use the oceans, seas and marine resources for sustainable development’ includes detailed objectives, such as fishery resource management and the conservation of marine ecosystems to preserve food resources in the ocean [6].

In response to these needs for EAF, many countries have studied and developed an ecosystem-based fisheries assessment and management approaches using risk assessment methods that are easy to apply in practice. In Australia, an ecological risk analysis for the effects of fishing (ERAEF) was developed and has been applied to Australian fisheries [7]. In addition, the Marine Stewardship Council (MSC) has been enforcing a certification system through ecosystem-based fisheries assessment [8]. An ecosystem-based fisheries assessment (EBFA) approach has been developed to evaluate the impact of fishing on the Korean marine ecosystem. The EBFA approach assesses risk scores for indicators corresponding to the four management objectives; sustainability, habitat quality, biodiversity, and socio-economic benefits, and estimates the nested indices of the species risk index (SRI), fisheries risk index (FRI), and ecosystem risk index (ERI) [9,10].

In previous studies on EBFA, some problems of the existing EBFA approach were identified and suggested to be resolved [11]. Socio-economic benefits were further added as one of the management objectives, and some overlapping components among indicators were clarified [10]. Socio-economic indicators have been studied and were incorporated with ecological indicators [12,13]. An revised risk scoring method for the EBFA was proposed that substituted the range of risk score zero to two with zero to three, and revised the risk scoring formulae [14]. A study of how to assess fisheries ecosystem in time and space scales by adding spatio-temporal components to the EBFA was developed, and this was applied to Korean waters [15]. Furthermore, a framework for an assessment, prediction, and management approach named IFRAME was developed based on EBFA [16].

The existing EBFA approach has been developed to focusing on offshore fishery and has been limited to assessing the effects of capture fisheries on wildlife fishery resources and their ecosystem. However, in the coastal ecosystems, there are two main ways to harvest fishery resources from the sea, wild capture fishery and aquaculture. Aquaculture has negative impacts on the ecosystem, including potential pollution, disease problems, and loss of habitat [17].

In addition, coastal ecosystems are seriously affected by various human activities, such as recreational fishing activities, land-based pollutant inflow, reclamation, and eroding coast lines [18,19], because they are adjacent to land. Wildlife fishery resources and their ecosystems are affected not only by capture fisheries but also by other driving forces, such as aquaculture, stock enhancement activities, land-based waste and pollution, inshore construction activities, leisure activities, and accidents and disasters (Figure 1). Therefore, in addition to capture fishery, the impacts of aquaculture and other human activities should also be considered especially when assessing coastal ecosystems.

In 2021, the South Korean government Ministry of Oceans and Fisheries (MOF) announced the ‘3rd Fisheries Resources Management Plan for 2021–2025’, including major challenges for the recovery of reduced fisheries resources, ecosystem-based fisheries resource assessment, and improvement of the coastal ecological environment [20]. In particular, South Korea has a long coastline of 14,963 km [21] because it is surrounded by sea on three sides and, therefore, has large coastal regions. In recent years, the proportion of aquaculture production in the total fishery production has annually increased and exceeding the production of wild capture fishery in 2006 [22]. Thus, in order to implement the new fisheries resources management plan properly, a suitable method considering these conditions in Korea should be developed urgently.

This paper aims to revise and extend the existing EBFA approach to apply to coastal ecosystems by incorporating various types of impacts caused by a number of anthropogenic activities other than wild capture fisheries. We conducted a demonstration by applying the extended EBFA approach to Uljin coastal waters and compared the applicabilities of the exsting EBFA and the extended EBFA.

2. Materials and Methods

2.1. Assessment Framework

The process of the extended EBFA approach is illustrated in Figure 2. The first step of the approach is to identify the unit ecosystem, fishery, and species. In this step, we investigate what components and characteristics of driving forces are in the ecosystem, what types of fisheries are operating in the ecosystem, and what kinds of fish species are residing and harvested in the ecosystem.

The second step is to define the impacts of the driving forces on fishery resources and ecosystems. Various driving forces, such as aquaculture, stock enhancement activities, land-based waste and pollution, inshore construction activities, leisure activities, and accidents and disasters can be defined as components of a unit ecosystem.

The third step is to identify the relevant pressures to the driving forces of the ecosystem and to develop proper indicators. These indicators should be prudently selected to represent the state of the fishery resources and marine ecosystems that are affected by each driving force. Since the driving forces and indicators vary by marine ecosystems, the extended EBFA adopted an on/off system for indicators, where each indicator can be active or inactive according to the marine ecosystem’s characteristics and components.

The fourth step is to evaluate the available information and scientific data and assess the risk of each indicator. If the available data are rich, Tier 1; quantitative assessment, is employed. On the other hand, if the available data are poor, Tier 2; semi-quantitative or qualitative approach, is used. In this step, the risk score (RS) is calculated for each indicator as

$$R{S}_i=\frac{I_{target}-I_i}{I_{target}-I_{limit}}+1,$$

(1)

where $R{S}_i$ is the risk score for indicator i, $I_i$ is a value of indicator i, $I_{target}$ is the target reference point for indicator i, and $I_{limit}$ is the limit reference point for indicator i. A higher RS implies a more risky status than does a lower RS. The minimum limit of a RS is zero, and the maximum limit is three. Thus, RS ranges from 0 to 3.

The fifth step is to carry out risk index analyses. Risk indices, such as the objective risk index (ORI), species risk index (SRI), fishery risk index (FRI), and ecosystem risk index (ERI), are calculated using RS estimated by a Tier 1 or Tier 2 assessment. The nested structure of risk indices used for a risk analysis in the extended EBFA approach is analogous to the previous EBFA approach [9], except for some additional components of the driving forces (Figure 3).

The objective risk index (ORI) can be calculated as

$$OR{I}_{S,B,H,E}=\frac{\sum W_{i}R{S}_i}{\sum W_i},$$

(2)

where ORI is calculated for each objective: $OR{I}_S$ for sustainability, $OR{I}_B$ for biodiversity, $OR{I}_H$ for habitat quality, and $OR{I}_E$ for socio-economic benefit. $W_i$ is the weighting factor for indicator i.

Since the extended EBFA assesses the effects of capture fisheries and other driving forces on a target species or their marine ecosystems, the species risk index by capture fisheries ($SR{I}_F$), and the species risk index by other driving forces ($SR{I}_O$) are respectively calculated as the same equation;

$$SR{I}_{F,O}={\lambda }_{S}OR{I}_S+{\lambda }_{B}OR{I}_B+{\lambda }_{H}OR{I}_H+{\lambda }_{E}OR{I}_E,$$

(3)

where ${\lambda }_S$, ${\lambda }_B$, ${\lambda }_H$, and ${\lambda }_E$ are the weighting factors for each management objective, and the sum of them is 1.0.

Then, the calculated species risk indices by capture fisheries ($SR{I}_F$) and other driving forces ($SR{I}_O$) are integrated as

$$SR{I}_j={\omega }_{F}SR{I}_F+{\omega }_{O}SR{I}_O$$

(4)

where $SR{I}_j$ is the SRI for species j. ${\omega }_F$ and ${\omega }_O$ are the weighting factors for capture fisheries and other driving forces, respectively, and the sum of them is 1.0.

The fishery risk index (FRI) is defined as

$$FR{I}_k=\frac{\sum B_{j}SR{I}_j}{\sum B_j},$$

(5)

where $FR{I}_k$ is the fishery risk index for fishery k, and $B_j$ is the biomass or the biomass index of species j.

The ecosystem risk index (ERI) is defined as

$$ERI=\frac{\sum C_{k}FR{I}_k}{\sum C_k},$$

(6)

where $ERI$ is the ecosystem risk index, and $C_k$ is the total catch of capture fishery k.

To assess the risk contribution levels on a target species of capture fisheries and other driving forces, the following equation can be used,

$$FR{C}_j=\frac{{\omega }_{F}SR{I}_F}{{\omega }_{F}SR{I}_F+{\omega }_{O}SR{I}_O}, OR{C}_j=\frac{{\omega }_{O}SR{I}_O}{{\omega }_{F}SR{I}_F+{\omega }_{O}SR{I}_O},$$

(7)

where $FR{C}_j$ is the capture fisheries’ risk contribution on species j, and $OR{C}_j$ is other driving forces’ risk contribution on species j.

In addition, risk indices can be estimated in various ways other than the above indices. For example, SRI, FRI, and ERI consider four management objectives; however, these risk indices can be calculated as $SR{I}_H$, $FR{I}_H$, and $ER{I}_H$, which consider only the habitat quality of the species, fishery, and ecosystem, respectively. For example, as shown in Figure 4, $SR{I}_H$ is calculated as

$$SR{I}_H={\omega }_{F}OR{I}_{HF}+{\omega }_{O}OR{I}_{HO}$$

(8)

where $SR{I}_H$ indicates the species risk index for the habitat quality incorporating effects of capture fishery and other driving forces. $OR{I}_{HF}$ is the objective risk index for the habitat quality of capture fishery. $OR{I}_{HO}$ is the objective risk index for the habitat quality of other driving forces.

Finally, the results from the risk index analysis can be utilized as measures to implement fisheries management at various management unit levels of species, fisheries, and ecosystems.

2.2. Application: Uljin Coastal Waters

To demonstrate the applicability of the extended EBFA, we selected a target ecosystem and estimated risk indices according to the procedure of Figure 2. In addition, they were compared with the results from the previous study using the existing EBFA.

2.2.1. Target Ecosystem, Fisheries, and Species

Uljin was selected as the target ecosystem, where the existing EBFA study was conducted [23]. Uljin is located in the east coast of South Korea, and has a 111.8-km long coastline [21]. The population is concentrated along the Uljin coast, and various activities, such as commercial capture fishery, recreational fishing, and aquaculture, are carried out in the adjacent waters. Although, in the previous study, a total of three fisheries and five species were selected to assess the Uljin coastal waters, only two target fisheries of gillnet and set net fisheries, and one target species of common squid (Todarodes pacificus) were selected to simplify the demonstration of the extended EBFA. The average annual catch amount of common squid by gillnet and set net fisheries were reported as 414 mt and 930 mt, respectively [23].

2.2.2. Driving Forces and Indicators

Excluding the impacts of wild capture fishery, which has already been studied, other driving forces and their indicators were defined for the application in consideration of Appendix A. First, a total of four factors, including leisure activities, aquaculture, land-based pollution, and accident and disaster, were identified as driving forces that have a significant impact on the fisheries resources and their ecosystems in the Uljin coastal waters. Next, we identified which pressures of each driving forces affect the ecosystem, and a total of nine indicators were selected in consideration of importance and the available information for assessment. The indicators were classified into the four management objectives of EBFA: sustainability, habitat quality, biodiversity, socio-economic benefit (

Table 1. Selected other driving forces and their indicators for the extended EBFA in the Uljin coastal waters.

Management Objective	Driving Force	Indicator
Sustainability	Leisure activities	Catch by leisure activities (S1)
	Accident and disaster	Deaths by disaster (S2)
Habitat quality	Aquaculture	Fish waste (H1)
	Land-based pollution	Domestic sewage (H2)
		Industrial sewage (H3)
		Industrial heated effluent (H4)
	Leisure activities	Waste by leisure activities (H5)
Biodiversity	Accident and disaster	Jellyfish bloom (B1)
Socio-economic benefit	Accident and disaster	Economic loss by Jellyfish (E1)

).

Two indicators, catch by leisure activities (S1) and waste by leisure activities (H5), especially regarding recreational fishing, were selected because this directly affects the sustainability of target species by generating pressure on fishing resources, and indirectly affects the habitat quality of coastal waters by generating environmental problems from abandoned fishing gear and bait [24,25].

Although various aquaculture effects have been reported [26,27,28], only one indicator of fish waste (H1) for aquaculture was selected considering reports on the damage cases and available information.

Based on the concentration of residential and industrial facilities along the Uljin coast, in particular, and nuclear power plant locations, three indicators for land-based pollution were selected: domestic sewage (H2), industrial sewage (H3), and industrial heated effluent (H4) [29,30]. As the indicators for the driving forces of accident and disaster, deaths by disaster (S2), jellyfish bloom (B1), and economic loss by jellyfish (E1) were chosen to consider a reduction in sustainability and biodiversity and also economic loss due to jellyfish bloom [31,32,33].

2.2.3. Risk Scoring and Derived Indices

Considering the method of previous case study of Uljin coastal waters [23] and available information; relevant reference points for the tier 1 approach have not been fully studied yet, so we adopted the tier 2 approach using semi-quantitative and qualitative assessment. RSs for eight indicators were estimated using relevant reports and quantitative statistical data [21,22,34,35,36,37] and by referring to the criteria table of risk states of the tier 2 approach adopted from the existing EBFA (

Table 2. Criteria of the risk states of the tier 2 ecosystem-based fisheries assessment (employed from the existing EBFA [10]).

Magnitude	Abundance	Condition	Likelihood	Frequency	Range	Risk Score
Extremely small	Never or None	Optimal, Best	High degree of uncertainty	Never	<5%	0.0
Small	Part or a few	Negligible	Highly unlikely	Rarely	5–20%	0.5
Moderately small	Some	Minor	Unlikely	Sometimes	20–40%	1.0
Average	Considerable or average	Moderate	Ambiguous	Average	40–60%	1.5
Moderately large	Many or Major	Major, significant	Likely	Often	60–80%	2.0
Large	Most	Severe, highly significant	Highly likely	Frequently	80–95%	2.5
Extremely large	All	Catastrophic, Worst	High degree of certainty	Always	>95%	3.0

, Table S4 in the Supplementary File).

The results of ${\mathrm{SRI}}_F$ for common squid harvested by gillnet and set net were obtained from the previous case study [23]. The ORIs for four management objectives ($OR{I}_S$, $OR{I}_H$, $OR{I}_B$, and $OR{I}_E$), the SRIs for other driving forces ($SR{I}_O$), the SRIs incorporating capture fishery and other driving forces for common squid harvested by gillnet ($SR{I}_G$) and set net ($SR{I}_S$), the FRIs for gillnet ($FR{I}_G$) and set net ($FR{I}_G$), and finally the ERI for Uljin coastal waters were calculated procedurally according to Equation (2)–(6), and compared with the results from the previous case study [23]. We assumed that the weights for all indicators and risk indices were the same.

3. Results

All RSs of selected indicators regarding the Uljin coastal waters were estimated as shown in

Table 3. The estimated risk scores and derived risk indices for Uljin coastal waters from the extended EBFA approach (RS: risk score for each indicator, ORI: objective risk index for each management objective, ${\mathrm{SRI}}_O$: species risk index by other driving forces, ${\mathrm{SRI}}_F$: species risk index by capture fishery, SRI:species risk index incorporating other driving forces and capture fishery, FRI: fishery risk index, ERI: ecosystem risk index, G: gillnet fishery, and S: set net fishery).

Management Objective	Indicator	RS		ORI		${\mathrm{SRI}}_O$		${\mathrm{SRI}}_F$		SRI(FRI)		ERI
		G	S	G	S	G	S	G	S	G	S
Sustainability	S1	1.5	1.5	1.5	2.0	1.8	2.2	2.4	1.7	2.1	2.0	2.0
	S2	1.5	2.5
Habitat quality	H1	1.5	2.5	1.7	2.3
	H2	1.5	1.5
	H3	2.0	3.0
	H4	2.0	3.0
	H5	1.5	1.5
Biodiversity	B1	2.0	2.0	2.0	2.0
Socio-economic benefit	E1	2.0	2.5	2.0	2.5

. Since recreational catch statistics were not available in Korea, the annual number of marine recreational fishing visitors by region was considered. As a result, 1.5 was given for the RS of catch by leisure activities (S1) because the number of marine recreational fishing visitors in Uljin was recorded at around the 40–45th percentile in Korea. Waste by leisure activities (H5) was scored at an RS of 1.5.

Recently on the Uljin coastal waters, due to the occurrence of red tide, the mass mortality of fish–especially in fish farms and set nets—has been frequently reported. Since set nets are fixed in the water and located in the vicinity of the fish farms, they are more affected than other fisheries by not only the direct effect of the death of the target species but also by the indirect effect of environmental damage from waste-farmed fish. Thus, 2.5 was assigned to the RS of death by disaster (S2) and fish waste (H1) for set net fisheries, whereas 1.5 was given to them for gillnet fisheries due to the fishing grounds being relatively less affected.

According to the statistics of sewage discharge, the total amount of domestic sewage increased by approximately twice as much; however, the amount of sewage that had not undertaken sewage treatment did not change significantly, and thus the RS of domestic sewage (H2) for gillnet and set was evaluated as 1.5.

On the other hand, the amount of industrial sewage, also having heated water problems, has nearly doubled in recent years, and cases of damage to fisheries due to the effects of eutrophication and heated effluent by industrial waste water have been frequently reported. Therefore, the RS of industrial sewage (H3) and industrial heated effluent (H4) for set net fishery was given as 3.0, and the RS for gillnet fishery was given as 2.0.

Lastly, based on the monitoring results of jellyfish bloom from the National Institute of Fisheries Science (NIFS) in South Korea, a number of jellyfish blooms have been reported due to the ecological effects of various human activities such as climate change and heated waste water. This phenomenon is known to have a great influence on the decrease in fishery production of target species and biodiversity. In particular, research reported that the economic loss by jellyfish bloom is more serious with regard to set net fishery. On the basis of these rationales, the RS of jellyfish bloom (B1) affecting the biodiversity for two fisheries was assigned as 2.0 equally. The Rs of economic loss by jellyfish was evaluated as 2.5 for set net fishery and 2.0 for gillnet fishery.

Using estimates of RSs, the nested indices: ORI, ${\mathrm{SRI}}_O$, SRI, FRI, and ERI, were calculated as shown in Table 3. The results of a previous case study were used as the ${\mathrm{SRI}}_F$s for gillnet and set net fisheries. As only one target species of common squid was selected for each fishery, the SRI and FRI were estimated to be the same value. From the previous case study of existing EBFA, the ${\mathrm{SRI}}_F$ of common squid for gillnet and set net fisheries were obtained as 2.4 and 1.7; however, the results of SRI(FRI) from the extended EBFA were estimated to be 2.1 and 2.0, respectively. Finally, ERI from the previous study, which was re-calculated with the selected target fisheries and species, and the extended EBFA values were estimated to be 1.9 and 2.0, respectively.

4. Discussion

We proposed an extended EBFA approach that incorporates the effects of capture fishery and other driving forces, including various human activities and natural processes. This paper focuses on (i) revising the process and equations related to nested risk indices defined in the existing EBFA approach and (ii) demonstrating the applicability of the proposed approach by applying it to Uljin coastal waters and comparing the results with the previous case study of the existing EBFA. The final goal of this paper is to emphasize that the intensive development of indicators and their relevant reference points for various driving forces are needed for practical use of the EBFA.

The results of application show that the ERI, ecosystem risk index for Uljin coastal waters, was higher than the ERI from the previous study. This indicates that the sum of FRIs or SRIs were estimated to be higher than the previous results due to additional considerations regarding other driving forces. Particularly, in the case of set net fisheries, the SRI was higher than the previous result (${\mathrm{SRI}}_F$) but for gillnet fisheries. This is because set net fisheries operate in a location that is more vulnerable to the impacts from other driving forces, such as land-based pollution and aquaculture. This shows that the impacts of driving forces vary by the type of fishery. This implication can be used as an example of supporting the necessity of the extended EBFA.

A number of integrated ecosystem modeling approaches considering many factors of marine ecosystems have been developed [38,39,40]. They have the advantage of simulating the complicated mechanism of marine ecosystems and, providing useful quantitative information for fisheries management. However, they require a great deal of input data and comprehensive knowledge to use them fully, and thus require much time and effort for practical use.

On the other hand, the proposed approach would be beneficial to save the time for applying if the tier 2 approach for semi-quantitative or qualitative analysis is employed. Furthermore, the approach with its on/off indicator system would allow the implementation of a fully integrated ecosystem-based fisheries assessment and the application to any marine ecosystem, not only offshore fisheries but also inshore, offshore, and distant fisheries. Given that the proper indicators for target ecosystem are developed, various marine ecosystems, such as a mangrove ecosystems, coral reef ecosystems, and marine protected areas (MPAs), can be examined by the approach.

Although many case studies applying the existing EBFA have been made to prove the practical applicability of EBFA [15,23,41,42,43,44,45], they have considered only the impacts of capture fisheries on ecosystems while ignoring the impacts of other driving forces. Recently, EFBA’s extensibility beyond fisheries to other human activities has been reported [46,47]. Taken together, this paper is a proactive study for developing a risk assessment tool incorporating various human activities with respect to the EBFA.

Using the nested risk indices of the extended EBFA, it would be straightforward to establish proper strategies and tactics by identifying which fishery/species/indicators are at high risk and deciding what means will be needed to reduce the risk of them. In addition, the proposed approach could be used as a integrated tool for achieving the goals of the ‘3rd Fisheries Resources Management Plan for 2021–2025’ implementing EBFM for sustainable fisheries announced by the Korean government [20].

However, indicators and their relevant reference points regarding other driving forces have not been fully developed yet in particular for the tier 1 approach due to the lack of scientific studies on relevant reference points. We preliminarily propose that potential reference points could be used for the two tier approaches (Tables S1–S4 in the Supplementary File) according to the guidelines described in the previous EBFA and other relevant research [9,10,48].

Although equal weighting factors were used for the simple applications in this study, the influence of the assigned weightings on the results would be significant in practical use. Therefore, as described in a previous study of EBFA, weightings should be carefully assigned by conducting a series of expert workshops or consultations, considering the importance of achieving the objectives, a scientific basis for estimating the indicators and reference points, the availability of data and information, and the characteristics of the target ecosystem [9].

This appears to be far from the practical applications of tier 1 thus far. Hence, further studies on the (i) development of indicators and their relevant target and limit references through practical application to various ecosystems, and (ii) integration of the extented EBFA and IFRAME [16] to allow the implementation of fundamental EBFM processes of assessment, forecasting, and management regarding various human activities are required to conduct practical application of the extended EBFA approach.

5. Conclusions

In this study, an ecosystem-based risk assessment approach incorporating the effects of capture fishery and other driving forces, including various human activities and natural processes, was proposed by revising the previous approach. The semi-quantitative and qualitative tier 2 approach of the extended EBFA was applied to Uljin coastal waters in South Korea. The results from application suggest a necessity of the extended EBFA to evaluate various types of ecosystems, including coastal waters, and applicability of the approach in the future.

In addition, we demonstrated that the benefits of the approach include less data and effort being required compared with other ecosystem models and the applicability to various ecosystems with the on/off indicator system. The approach would be straightforward to establish proper strategies and tactics to address risky pressures and driving forces. However, more research remains to be done on the indicators and reasonable reference points, in particular, to the tier 1 approach before the extended EBFA can be used in practical application.