Development of a Gear-Based Fisheries Management Index Incorporating Operational Metrics and Ecosystem Impact Indicators in Korean Fisheries

Abstract

Traditional single-species fisheries management has proven inadequate for capturing ecosystem interactions, leading to a shift toward ecosystem-based approaches. In Korea, diverse small- and medium-scale with varying gear types, production volumes, and practices require management tools that address both ecological and industrial needs. This study developed a Gear-based Fisheries Management Index (GFMI) for 24 coastal and offshore fisheries in Korea. The framework, based on the “ideal gear attributes” defined by ICES, is structured around three objectives: gear controllability, environmental sustainability, and operational functionality. Sub-indicators and weights were derived through expert consultation using the Analytic Hierarchy Process and standardized with Z-scores from national statistics, including production volume, license numbers, and accident rates. Results show that in coastal fisheries, coastal gillnets (61.7) and coastal improved stow nets (60.7) recorded the highest scores, largely due to negative impacts such as bycatch, reproductive capacity, and gear loss. Coastal purse seines (40.9) received the lowest score, reflecting species selectivity advantages. In offshore fisheries, large bottom pair trawls (71.8) and Southwestern medium-size bottom pair trawl (69.3) ranked highest, indicating strong habitat impacts. While coastal improved stow nets, large purse seines, and large trawls performed well in operational functionality, high costs and efficiency constraints remain key vulnerabilities.

1. Introduction

Fisheries management, implemented since the early 1940s, has primarily focused on controlling human intervention through fishing activities and target resources [1]. This traditional approach, which has often relied on non-gear-based tools like quota allocations, spatial restrictions, and minimum landing sizes, has failed to address issues arising from the non-exclusive use of fishery resources [1]. Consequently, excessive competition among fishers has emerged [1], and such practices have led to unintended habitat destruction, incidental mortality of non-target species, evolutionary changes within populations, and structural alterations of ecosystems [2]. Moreover, management strategies aimed at maximizing the catch of a single species [3,4,5,6,7,8] have overlooked that species’ interactions with its habitat, predators, prey, and other ecosystem components [2].

To address these challenges, ecosystem-based fisheries management (EBFM) has been advanced as a holistic approach that recognizes the complex dynamics among target and non-target species and the broader social–ecological system [9]. Garcia et al. [10] define EBFM as an approach that considers key ecosystem components and services both structural and functional when managing fisheries. Nevertheless, even with EBFM explicitly incorporates ecosystem interactions, contemporary fisheries management remains largely grounded in single-species stock assessments [11].

A core principle of modern fisheries management including both single-species and ecosystem-based approaches, is the precautionary principle. While this principle can be applied to manage single-species stocks, it is a foundational tenet of EBFM, which applies it comprehensively to safeguard not only target populations but entire ecosystems [12]. This principle entails proactively halting destructive fishing practices and ensuring the long-term health of marine resources. In the Republic of Korea, declines in the average trophic level of marine ecosystems coincide with overfishing [13], underscoring the central role of fisheries management in conserving marine resources.

Fisheries scientists were pivotal in the shift toward EBFM, yet in many respects policy has outpaced the maturation of the scientific knowledge and operational tools needed for implementation [14]. Moreover, although EBFM is valued for indicator-based assessments of complex, fluctuating ecosystems, its uptake is constrained by limited data, unclear institutional responsibilities, and uneven adoption across jurisdictions [15]. In particular, the Republic of Korea‘s fisheries, characterized by a diverse mix of gear types and small-scale operations [16,17], have previously shown significant limitations in the application and expansion of fishery resource management systems such as the total allowable catch [16]. Specifically, with over 41 coastal and offshore fisheries operating under the Enforcement Decree of the Fisheries Act, the same species are caught with a variety of gear. Each gear has a different impact on the ecosystem depending on its fishing method and intensity. Consequently, existing species-centric models struggle to systematically compare and assess the impacts of these heterogeneous fisheries.

As previously noted, Smith et al. [14] highlighted limitations inherent in ecosystem-based fisheries assessment (EBFA) and set out three instruments to address them. The Ecological Risk Assessment for the Effects of Fishing (ERAEF) is a tiered framework that proceeds from expert elicitation to semi-quantitative screening and, where warranted, to quantitative modeling; its staged design reduces cost while retaining diagnostic power and practical applicability. Management Strategy Evaluation (MSE) constructs and evaluates alternative management strategies across ecological, economic, and social objectives, providing an explicit basis for trade-off analysis and for judging implement ability [14]. The Harvest Strategy Framework (HSF) specifies harvest control rules across four information tiers, thereby making the treatment of uncertainty explicit [14].

Building on this line of work, Zhang et al. [18,19] applied a quantitative EBFA tailored to Korea by combining multi-level risk indices with ecosystem models. These methods, however, are data-intensive and technically demanding, and they are organized around species or stocks rather than gears or sectors; as a result, systematic comparisons across industries are cumbersome, particularly given the heterogeneity of gears in Korea’s coastal and offshore fisheries. Furthermore, fishery resource assessment models like EBFA, while adept at evaluating long-term ecosystem and climate-related risks, demonstrate limitations in providing policymakers with clear, gear-specific, and granular evaluations. In other words, while the EBFA model is indispensable for long-term strategic planning and comprehending complex ecological dynamics, a more efficient and practical instrument is required for operational management within the heterogeneous and complex structure of the Korean fisheries industry.

Accordingly, this study takes fishing gear as the unit of analysis and proposes a Gear-based Fisheries Management Index (GFMI), derived from the ICES [20] “ideal gear attributes.” The GFMI operationalizes gear-specific management by summarizing attributes linked to ecological sustainability, ecosystem effects, regulatory compliance, and socio-economic performance. It incorporates sectoral scale, production, and operating characteristics of Korean fisheries and provides a basis for comparative appraisal of each sector’s current management implications.

2. Materials and Methods

2.1. Framework for Constructing a Gear-Based Fisheries Management Index

The Gear-based Fisheries Management Index (GFMI) consisted of three components objectives, sub-indicators, and weighting factors (Figure 1). In accordance with ICES [20], the “ideal gear attributes” were defined to systematize the appraisal of gear types and to identify responsible fishing practices (

Table 1. Definitions of ideal fishing gear attributes (ICES [20]).

Category	Attribute	Definition (Ideal Practice)
Catch controllability	Quality of catch	Minimize physical damage during capture (thereby shortening ecosystem recovery time) to maximize catch quality.
	Species selectivity	Capture only target species; minimize bycatch.
	Size selectivity	Capture individuals within a specified size (e.g., length) range; exclude smaller and larger fish.
Environmental impact and sustainability	Habitat impact	Minimize habitat disturbance during gear deployment and use; account for potential ghost fishing.
	Energy cost	Minimize energy use in vessel operation and navigation to reduce the carbon footprint.
	Bycatch (non-commercial species)	Minimize catches of non-commercial species, especially those that are threatened or endangered.
	Catch welfare	Minimize physical injury and physiological stress at capture to reduce discard losses.
Operational functionality	Safety	Minimize injuries and fatalities to fishers associated with gear use.
	Durability	Ensure sufficient service life of gear, including reasonable maintenance and repair requirements/costs.
	Gear costs	Keep upfront capital costs low to enable rapid gear replacement when required by management.
	Ease of use	Require minimal training and allow rapid gear substitution in response to management directives.
	Applicability	Usable across seasons and environmental conditions in a broad range of aquatic habitats.

). Three domains catch controllability, environmental sustainability, and operational functionality were specified, reflecting the aim of minimizing fishery wide ecosystem impacts and securing long-term sustainability (Table 1).

These objectives were operationalized through sub-indicators employing quantitative and qualitative metrics spanning ecological, social, economic, and governance dimensions. Weighting factors were designed and applied to incorporate industry conditions in Korea sector-specific scale, production, and operating characteristics that were not directly captured by the sub-indicators. Consequently, the index was structured to express, as a score, the extent to which each sector influences current fisheries management.

Objectives, sub-indicators, and weighting factors were derived from on-site surveys of fishing organizations and from a panel of 41 experts in fishery resources and the fishing industry (Figure 2). In addition, a targeted literature review was conducted to inform indicator selection for the construction of the GFMI.

2.2. Fisheries Management Index Calculation Procedure

2.2.1. Target Fisheries

The GFMI was calculated for 24 sectors selected from 41 coastal and offshore fishery categories in Korea (

Table 2. Fishery types for the GFMI analysis.

Category	Fishery Types
Coastal fisheries (6)	Coastal improved stow net fishery Coastal purse seine fishery Coastal trap fishery Coastal beam trawl fishery Coastal gillnet fishery Coastal composite fishery
Offshore fisheries (18)	Large Danish seine fishery Large bottom pair trawl fishery Eastern area medium-size Danish seine fishery Southwestern medium-size Danish seine fishery Southwestern medium-size bottom pair trawl fishery Large trawl fishery Eastern area medium-size trawl fishery Large purse seine fishery Small purse seine fishery Offshore jigging fishery Offshore gillnet fishery Offshore stow net fishery Offshore trap fishery Offshore longline fishery Offshore dredge fishery Anchovy boat seine fishery Offshore eel trap Diving fishery

). Selection was based on the availability of domestic and international research, governmental reports, and statistical series sufficient to estimate detailed indicators and weighting factors. The final sample comprised 18 offshore sectors and 6 coastal sectors (Table 2).

2.2.2. Analytic Hierarchy Process (AHP) Based Weighting Calculation

To derive the Fisheries Management Index, a hierarchical structure was specified using the Analytic Hierarchy Process (AHP; [21]). Pairwise comparisons were performed to elicit relative priorities across tiers: Tier 1 comprised the objectives, and Tier 2 comprised the sub-indicators and weighting elements.

The pairwise comparison matrix was constructed by evaluating the relative importance of each element $i$ and $j$ on a scale of 1 to 9, forming an $\mathrm{n}\times \mathrm{n}$ matrix $\mathrm{A}=\left[a_{ij}\right]$. The weights were then calculated using eigenvectors, yielding a weight vector $\mathrm{w}={(w_1,w_2,\dots ,w_n)}^T$ using Equation (1).

$$\mathrm{A}\mathrm{w}={\lambda }_{max}w$$

(1)

Here, ${\lambda }_{max}$ is the maximum eigenvalue of the matrix, and $w$ is normalized so that ${\sum }_{i=1}^n{w}_i=1$. The weights were calculated using the geometric mean method using Equation (2) below.

$$w_i={\displaystyle \frac{{\left({\prod }_{j=1}^n{a}_{ij}\right)}^{1/n}}{{\sum }_{k=1}^n{\left({\prod }_{j=1}^n{a}_{kj}\right)}^{1/n}}}$$

(2)

To verify consistency, the consistency index (CI) and consistency ratio (CR) were used to evaluate the consistency of expert judgment.

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}, \mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

(3)

To ensure reliability, all expert response matrices were required to meet a consistency ratio (CR) < 0.1; responses exceeding this threshold were subjected to re-survey. In addition, expert judgments were aggregated using the geometric mean to reduce the influence of outliers while preserving the ratio scale.

2.2.3. Standardization of Indicator and Weighting Factor Value

Because units and ranges differed across indicators and weighting factors, z-score standardization was applied (Equation (4)), prior to weighting, to mitigate scale effects and prevent undue leverage of any single indicator. Standardization was performed for statistical variables (e.g., production volume) among the sub-indicators and weighting factors; index-type values were excluded from standardization.

$$Z_i={\displaystyle \frac{X_i-\mu }{\sigma }}$$

(4)

Here, $X_i$ is the indicator value of the industry being evaluated, $\mu$ is the mean of the same category (offshore fisheries, coastal fisheries), and $\sigma$ is the standard deviation. The calculated $Z$-value was converted to a percentage using a standard normal distribution table, and then multiplied by the score of each table to obtain a score.

2.2.4. GFMI Calculation

The sub-indicator score was calculated by multiplying the corresponding indicator value by its weight and summing the results to produce the sub-indicator score ($S_{ij}$, Equation (5)).

$$S_{ij}={\sum }_{k=1}^n(V_{ijk}\times W_{ijk})$$

(5)

Here, $S_{ij}$ represents the score of sub-indicator $j$ for fishery type $i$, $V_{ijk}$ represents the evaluation criterion $k$ (standardized index value or raw data) for fishery type $i$, and $W_{ijk}$ represents the weight of the evaluation criterion. The scores for each higher-level objective were calculated by multiplying the sub-indicator scores by their weights (Equation (6)).

$$M_i={\sum }_{j=1}^m(S_{ij}\times P_j)$$

(6)

Here, $M_i$ is the upper target score for fishery type $i$, and $P_j$ is the sub-indicator score. The final GFMI was calculated by summing the three upper target scores (Equation (7)).

$${\mathrm{G}\mathrm{F}\mathrm{M}\mathrm{I}}_i={\sum }_{g=1}^3{M}_{ig}$$

(7)

Here, ${\mathrm{G}\mathrm{F}\mathrm{M}\mathrm{I}}_i$ is the final fisheries management index of fishery type $i$.

3. Results

3.1. Elements of the GFMI

The GFMI was derived from the ICES [20] ideal fishery attributes and was informed by fieldwork, expert interviews, and a targeted literature review. Objectives were adapted from the ICES elements; “catch controllability” was relabeled “fishing-gear controllability” to emphasize a gear-based management perspective. Sub-indicator nomenclature was revised, in consultation with practitioners and subject-matter experts, to clarify construct meaning; redundant items (e.g., species selectivity and size selectivity) were consolidated or redefined. Gear-specific elements were also incorporated to reflect characteristics of the fishing gear.

Evaluation criteria for the sub-indicators were specified quantitatively using published sources and official statistics. For qualitative constructs (e.g., fishing mechanisms), benchmarks were elicited from practitioner and expert judgment. Weighting factors consisted of socio-economic modifiers that influence the interpretation of sub-indicator performance.

3.2. Weights by GFMI Elements

The weights for the GFMI elements listed in

Table 3. Sub-indicators, evaluation criteria, and weighting factors for each objective in GFMI calculation.

Objective	Sub-Indicator	Evaluation Criteria	Weighting Factors
Gear controllability	Reproductive capacity of resource	Reproduction index (juvenile catch rate, size selectivity)	CPUE (production per unit engine power), No. of licenses/licensed vessels, Socio-legal factors (closed seasons, gear restrictions, prohibited fishing zones, illegal fishing activities)
	Species selectivity	Target species catch rate [20]	Fishing power (fleet capacity)
	Catch mechanism (gear type characteristics)	Gear-type index (by fishery)	Value per unit catch, No. of violations related to illegal fishing gear
Environmental sustainability	Habitat Impact	Habitat impact index [22]	No. of fishing days, No. of violations of fishing/prohibited zones
	Energy cost per unit production	Energy cost per unit production [23]	Total production (by fishery)
	Bycatch	Bycatch rate [24]	Total production (by fishery)
	Discard	Discard rate [25]	Total production (by fishery)
	Fishing gear loss	Gear loss risk index [26]	Fishing power (fleet capacity)
Operational functionality	Crew safety	Crew accident rate [27]	Fishing power, No. of fishing days, Total no. of crew, Proportion of foreign crew
	Gear durability	Gear durability index [28]	Gear loss risk
	Gear cost	Annual gear cost [29]	-
	Ease of gear operation	Gear operation ease index	Total no. of crew
	Ease of fishing operations (fishing ground and season)	Fishing operation ease index	No. of fishing days

were estimated using the AHP and are summarized in

Table 4. Weights by GFMI element.

Objective	Sub Indicator (Indicator Score)	Evaluation Criteria (Weight)	Weighting Factors (Weight)
Gear Controllability (50)	Reproductive capacity of resources	Reproduction index	CPUE	No. of licenses/licensed vessels	Socio-legal factors
	(20)	(0.51)	(0.3)	(0.15)	(0.04)
	Species selectivity	Species selectivity index	Fishing power (fleet capacity)
	(15)	(0.6)	(0.4)
	Catch mechanism	Gear type index	Unit cost of production	No. of violations related to illegal fishing gear
	(15)	(0.65)	(0.3)	(0.05)
Environmental sustainability (30)	Habitat impact	Habitat impact index	No. of fishing days	No. of violations of fishing/ prohibited zones
	(10)	(0.65)	(0.3)	(0.05)
	Energy Consumption	Energy cost per unit production	Total Production
	(3)	(0.6)	(0.4)
	Bycatch	Bycatch rate	Total production
	(6)	(0.6)	(0.4)
	Discard	Discard rate	Total production
	(3)	(0.6)	(0.4)
	Fishing gear loss	Gear loss risk index	Fishing power (fleet capacity)
	(8)	(0.6)	(0.4)
Operational functionality (20)	Crew safety	Accident rate	Fishing power (fleet capacity)	No. of fishing days	Total no. of crew	Proportion of foreign crew
	(4)	(0.5)	(0.2)	(0.2)	(0.05)	(0.05)
	Gear durability	Gear durability index	Gear loss risk
	(4)	(0.6)	(0.4)
	Gear cost	Annual gear purchase cost
	(4)	(1.0)
	Ease of gear operation	Gear operation ease index	Total no. of crew
	(4)	(0.7)	(0.3)
	Ease of fishing operation	Fishing operation ease index	No. of fishing days
	(4)	(0.6)	(0.4)

. Within the gear-controllability objective, the resource reproductive capacity indicator received the highest single-indicator weight (20 points), underscoring its centrality for sustainable fisheries. Within environmental sustainability, habitat impact obtained the highest weight. By contrast, all operational functionality indicators were assigned 4 points each.

3.3. Application to Target Fisheries

3.3.1. Coastal Fisheries

For the fishing-gear controllability objective in coastal fisheries (Figure 3a), the coastal beam trawl fishery obtained the highest score (32.8). Among the higher-scoring types, the coastal improved stow net fishery was penalized primarily for adverse effects on resource reproductive capacity, while the coastal gillnet fishery was additionally penalized for species selectivity. By contrast, the coastal purse seine fishery recorded the lowest score (21.3), reflecting comparatively favorable species selectivity.

Under environmental sustainability (Figure 3b), the coastal gillnet fishery recorded the highest GFMI (19.9), whereas most other fishery types clustered at similar levels. The gillnet fishery was particularly penalized for gear loss and bycatch. The lowest score occurred in the coastal purse seine fishery (5.8).

For operational functionality (Figure 3c), the coastal improved stow net fishery achieved the highest score (14.5), followed by the coastal purse seine fishery and the coastal gillnet fishery. The improved stow net and purse seine fisheries scored highly on gear cost and ease of operation, indicating vulnerabilities in these aspects, while the gillnet fishery was notably vulnerable in gear durability. Conversely, the coastal composite fishery recorded the lowest score (8.7).

Based on the GFMI results for coastal fisheries (Figure 4), the coastal gillnet fishery achieved the highest score (61.7), followed by the coastal improved stow net fishery (60.7). By contrast, the coastal purse seine fishery recorded the lowest GFMI among the coastal fishery types assessed.

3.3.2. Offshore Fisheries

For the gear-controllability objective (Figure 5a), the Southwestern medium-size bottom pair trawl recorded the highest total (36.2). Other high-scoring types included the anchovy boat seine, offshore stow net, large bottom pair trawl, and Eastern area medium-size trawl. Elevated totals in this domain reflect penalties associated with reproductive capacity, species selectivity, and catch mechanism. At the lower end, the diving fishery had the smallest total, followed by the small purse seine and offshore jigging, indicating comparatively favorable gear controllability.

For environmental sustainability (Figure 5b), the large bottom pair trawl ranked first (22.5), followed by the offshore stow net, Southwestern medium-size Danish seine, Southwestern medium-size bottom pair trawl, large Danish seine fishery, and offshore gillnet. In these fisheries, high totals were driven chiefly by habitat impact, with additional contributions from bycatch, discards, and gear loss. The lowest totals were observed for the Eastern area medium-size trawl (6.9), small purse seine (7.3), and diving (7.9).

For operational functionality (Figure 5c), the large bottom pair trawl again ranked highest (16.2), followed by the large purse seine and the Southwestern medium-size bottom pair trawl. Their totals were elevated by higher scores for gear cost, gear handling, and operation ease, indicating operational vulnerabilities. The diving fishery showed the lowest total (7.5).

Analysis of the fisheries management index (Figure 6) for offshore fisheries showed that the large bottom pair trawl fishery had the highest score (71.8), followed by the Southwestern medium-size bottom pair trawl fishery (69.3). By contrast, the diving fishery, small purse seine fishery, and offshore jigging fishery recorded much lower index values, indicating relatively favorable outcomes in terms of fisheries management performance.

4. Discussion

The GFMI developed in this study revealed gear-specific characteristics and vulnerabilities across Korea’s coastal and offshore fisheries. In the coastal fisheries, the coastal gillnet fishery and coastal improved stow net fishery showed relatively high overall GFMI scores, largely reflecting penalties on reproductive capacity, bycatch, and gear loss. Consistent with this, Gilman et al. [26] scored the risk of abandoned/lost gear on a 0–1 scale and reported gillnets = 1.0, the highest loss risk. National statistics likewise indicate substantial bycatch burdens: gillnets accounted for 71.2% of recorded bycatch and longline gears for 57.3%, the highest shares among coastal gears [24]. By contrast, the coastal purse seine fishery had the lowest overall GFMI, reflecting comparatively favorable performance in species selectivity; in ICES [20], purse seines were assigned a risk score of 0 for species selectivity.

Second, in offshore fisheries, large bottom pair trawls and Southwestern medium-size bottom pair trawl scored high. This indicates that trawl fishing operations scored high on sub-indicators such as habitat impact, reproductive capacity, and fishing mechanisms. Fuller et al. [22] evaluated the marine habitat impact of problematic fishing gear in Canadian fisheries on a five-point scale from low to high based on fishing mechanisms. Bottom trawl and dredge fishing operations scored a high score of 5, indicating a high impact on the seafloor.

Third, in terms of operational functionality, coastal improved stow nets, coastal purse seines, and coastal large trawl fishing operations scored relatively high. This suggests vulnerabilities in gear cost and operational ease.

Our findings provide a direct basis for developing goal-oriented, gear-specific policies to address industry vulnerabilities identified in this study. For fisheries with high GFMI scores, such as coastal gillnet and coastal improved stow net fisheries, our results suggest the following policy considerations.

To improve selectivity, given their poor performance in species selectivity and reproductive capacity, policies could encourage the use of larger-mesh gillnets or require escapement devices in stow nets to reduce the catch of non-target and juvenile species. To prevent gear loss, the high risk associated with gillnets suggests a need for policies that mandate biodegradable panels or require a comprehensive gear registration and tracking system. To promote gear substitution, the notable contrast between high-scoring gillnet and low-scoring coastal purse seine fisheries highlights a policy opportunity to incentivize a transition from less-selective, higher-impact gear types to more sustainable alternatives. The GFMI provides the quantitative evidence to justify such a shift. Lastly, to enhance crew safety, for fisheries with operational vulnerabilities like high gear costs or complexity, policies could focus on providing subsidies for safety equipment or mandating regular safety training to reduce operational risk.

Ultimately, the GFMI provides the evidence to support a nuanced, multi-faceted approach that addresses specific vulnerabilities, rather than a broad, single-instrument policy like a total ban on certain gear types.

As summarized in

Table 5. Comparative analysis of GFMI and previous studies on gear impacts and fisheries management.

Category	GFMI Study (This Study)	Chuenpagdee et al. [12]	ICES [20]	Fuller et al. [22]	Potts [15]	Zhang et al. [18]	Zhang et al. [19]
Objective	Development of a Gear-based Fisheries Management Index (GFMI) for Korean coastal and offshore fisheries, integrating sustainability, ecosystem impact, and operational functionality	Assessment of the collateral impacts (e.g., bycatch, habitat damage) of major fishing gears in the U.S. and prioritization of management actions	Scientific advice on gear selectivity, alternatives, and technology development	Evaluation of ecological impacts of Canadian gears and provision of policy recommendations	Development of a sustainability indicator system (SIS) and policy framework	Ecosystem-Based Fisheries Assessment (EBFA) of Korean fisheries focusing on sustainability, biodiversity, and habitat quality	Evaluation of fisheries and ecosystem risks under climate change scenarios
Unit of analysis	24 coastal and offshore fisheries in Korea (gear-based index)	10 gear types	15 fishing technologies/gear types	Major Canadian fishing gears	Policy/indicator system at national and international levels	Tier 1 (quantitative), Tier 2 (qualitative) → species, fishery, ecosystem units	Species–fishery–ecosystem–socioeconomic units combined with climate scenarios
Methodology	AHP-based weighting + Z -score standardization; calculation of sub-indicators → GFMI score	- Literature review (170 studies) + expert workshop (13 experts) - Damage Schedule: gear impacts scored on a 1–5 scale	Qualitative scoring (0–2) on controllability, sustainability, functionality	Literature review + surveys + workshops → severity ranking analysis	Input–Structure–Output framework; application to policy case studies	Development of risk indices (ORI, SRI, ERI, FRI) and MSI; risk diagram approach	Coupled models (NEMURO + Ecopath/Ecosim); risk indices within Assessment–Forecast–Management procedure
Geographic scope	Coastal and offshore fisheries in Korea	U.S. nationwide	ICES regions (e.g., North Sea)	East and West Coast of Canada	International and national policy comparisons (e.g., Australia, MSC)	Korean fishing grounds (e.g., Tongyeong, purse seine fisheries)	Korean Peninsula and North Pacific ecosystems
Data demand	Statistical data (production, licenses, accidents) + expert judgment → moderate	Quantitative data on bycatch and habitat damage (from literature) + expert/fisher opinions → moderate	Technical reports and experimental data → high	Literature and survey data → moderate	Policy and institutional data → low	Tier 1 quantitative + Tier 2 qualitative data → diverse requirements	Very high (integration of climate, ecological, fishery, and socioeconomic data)
Main outputs	Gear-specific GFMI scores (comparisons across coastal/offshore	- Relative Severity Ranking: High, Moderate, Low - Policy categories suggested (ban/improve/mitigate)	Identification of “responsible gears” (e.g., diving, pole and line, pots) and vulnerable gears (trawls, dredges)	Severity ranking of gear impacts + policy recommendations	Policy analysis framework + evaluation checklist	Risk indices (ORI, SRI, ERI, FRI), MSI; comparative assessment of risks	Climate-scenario-based risk indices; evaluation of management strategies (HCRs, MPAs)
Management use	Provides management priorities and policy alternatives for Korean fisheries	- Strict regulation of destructive gears - Transition to less destructive gears	Revision of gear classification; advice on alternative gears	Provides evidence for Canada’s EBM and gear policy design	Policy tool for national/international sustainability planning	Assessment of species, fishery, and ecosystem conditions; management performance comparison	Supports climate adaptation strategies, harvest control rules (HCRs), and MPA design

, the GFMI was benchmarked against established assessment frameworks for fishing gears and fisheries management. Chuenpagdee et al. [12] provided one of the earliest quantitative appraisals of collateral impacts bycatch and habitat disturbance across major gear types in U.S. waters and proposed management priorities. Their approach ranked the relative severity of impacts using a gear impact schedule derived largely from expert elicitation; however, dependence on expert judgment and an essentially one-dimensional scoring scheme limited reproducibility and integrative applicability. ICES [20] likewise offered guidance on gear selectivity and technological alternatives, but its geographic focus (e.g., the North Sea) and advisory orientation constrained its use as a comprehensive, integrated management indicator.

Fuller et al. [22] assessed the severity of gear impacts through workshops and surveys in Canadian fisheries, thereby providing groundwork for ecosystem-based management (EBM) and policy design. By contrast, Potts [15] developed a sustainability indicator system (SIS) at the policy and institutional level; while useful for governance evaluation, it is limited in resolving gear and industry specific (fishery types) impacts. In Korea, Zhang et al. [18,19] implemented an ecosystem based fisheries assessment (EBFA) that combined multi-level risk indices with ecosystem models and evaluated long term management strategies incorporating climate change scenarios. This approach, however, is data intensive and technically complex, which constrains its routine application in policy settings.

In contrast, the GFMI developed here provides a quantitative, gear and fishery type level assessment. Whereas frameworks such as EBFA and IFRAME (Integrated Fisheries Risk Analysis Method for Ecosystems) are organized around species or ecosystems hindering like for like comparisons across gears and industries the GFMI treats 24 gear-based fishery types in Korea’s coastal and offshore fisheries as the units of analysis, enabling cross-gear comparisons and furnishing a practical basis for prioritizing management actions. Grounded in the ICES [20] “ideal gear attributes,” the GFMI delineates three objective domains gear controllability, environmental sustainability, and operational functionality and integrates expert elicitation (AHP) with official statistics (e.g., production, license counts, accident rates) to quantify both qualitative and quantitative dimensions of fishing impacts. By explicitly incorporating sectoral scale, production, and operating characteristics, the index captures the heterogeneity of Korea’s fisheries and embeds industrial context alongside ecological performance, thereby enhancing its policy relevance and practical applicability.

However, the GFMI has several limitations. First, it does not explicitly model species ecosystem interactions. Whereas EBFA and IFRAME incorporate predator prey dynamics, habitat change, and climate forcing, the GFMI is a gear level assessment that abstracts from these processes. Second, it is sensitive to data availability and imbalance. Some indicators can be quantified from national statistics, but others (e.g., accident rates, gear durability) rely on expert elicitation; uneven coverage across fishery types may affect comparability. Third, international comparability is limited: the index is tailored to Korea’s fleet structure and gear characteristics and is not intended as a universal metric. Fourth, temporal responsiveness is weak: because the index is computed from cross sectional data, it does not readily capture technological change, climate variability, or long run shifts in stock abundance.

The methodologies used in this study have several limitations. First, the AHP inevitably relies on subjective judgment to derive weights [30], as it follows a hierarchical process from objectives to criteria, sub-criteria, and alternatives [21]. While this study used pairwise comparisons and consistency ratios to ensure the reliability of expert judgments, personal biases or perspectives could still influence the results. A more precise way to handle ambiguous judgments is the fuzzy AHP method, which combines fuzzy sets [31] and AHP. This method involves setting a comparison matrix, aggregating multiple judgments, measuring consistency, and then defuzzifying fuzzy weights [30].

Second, AHP’s strict hierarchical structure may oversimplify the complex and interconnected real-world fishery management system, which includes ecosystems, economies, and societies. It may not adequately capture feedback loops or non-hierarchical relationships. While the GFMI provides a practical assessment at the gear level, overcoming these limitations requires an integrated approach. Future research could consider using the Analytical Network Process (ANP), a generalization of AHP that models interactions and feedback among elements [32].

In conclusion, the GFMI provides an intermediate alternative between qualitative, expert-only screening tools and data intensive ecosystem models. Its modest data requirements enable routine policy use, and its multidimensional structure integrates ecological, technical, and operational considerations at the gear level. In fisheries such as Korea’s marked by heterogeneous small and medium sized sectors the GFMI offers actionable, gear-specific diagnostics and supports the design of multidimensional management options.

Future work should incorporate explicit ecosystem dynamics (trophic and habitat linkages), strengthen data driven estimation, improve cross-national comparability through calibration, deploy time series applications for trend detection, and integrate socioeconomic dimensions. With these extensions, the GFMI could evolve into a practical, transferable fisheries management instrument with relevance beyond the national context.

5. Conclusions

This study presents a Gear-based Fisheries Management Index (GFMI) that evaluates 24 coastal and offshore fisheries in Korea using the ICES (International Council for the Exploration of the Sea) “ideal fishing gear attributes.” The index quantifies gear- and industry-specific management vulnerabilities, where higher scores indicate greater management risks and burdens. In coastal fisheries, gillnets (61.7) and improved stow nets (60.7) scored highest due to bycatch, reduced reproductive capacity, and gear loss, whereas coastal purse seines (40.9) scored lowest owing to advantages in species selectivity. In offshore fisheries, large bottom pair trawls (71.8) and Southwestern medium-size bottom pair trawl (69.3) scored highly, reflecting environmental impacts such as habitat disturbance. In terms of operational functionality, improved stow nets, large purse seines, and large trawls showed vulnerabilities related to costs and ease of operation. Based on these results, the GFMI provides a practical basis for prioritizing multidimensional management actions, including improving selectivity, promoting gear substitution, preventing gear loss, and enhancing crew safety.

However, the GFMI has limitations: (1) it does not explicitly model species–ecosystem interactions; (2) indicator data are imbalanced; (3) its Korea-specific design limits international comparability; and (4) its cross-sectional focus reduces time-series sensitivity. Future work should broaden the index’s generality and policy utility by integrating trophic and habitat connectivity, strengthening data-driven estimation, establishing cross-national calibration, and expanding applications to time-series and socioeconomic indicators.