Management strategy evaluation for real-time closures in the short mackerel fishery in the Gulf of Thailand

This study demonstrates that real-time closures (RTCs) are more effective than static closures for managing the Gulf of Thailand's short mackerel fishery, as they offer greater flexibility, require smaller closure areas, and successfully mitigate fishing mortality for migratory species by utilizing near real-time surveillance data.

The Gist

Imagine the Gulf of Thailand as a giant, bustling highway for a specific type of fish called the Short Mackerel. These fish are like commuters; they don't stay in one spot. They migrate, swim in schools, and move around based on the weather and where the food is.

For years, the government tried to protect these fish using Static Closures (STCs). Think of this like putting up a permanent "Road Closed" sign on a specific stretch of highway every year from February to May, regardless of whether the traffic (the fish) is actually there. It's simple to enforce, but it's inefficient. Sometimes the fish aren't there, so you're blocking boats for no reason. Other times, the fish have moved to a different part of the highway, and the "Road Closed" sign is useless because the traffic is flowing right past it.

This paper asks a big question: Can we do better?

The authors propose using Real-Time Closures (RTCs). Instead of a permanent sign, imagine a smart traffic control system that uses live cameras and sensors (called Vessel Monitoring Systems and landing reports) to see exactly where the fish are right now. If a huge school of fish is spotted in Zone A, the system instantly puts up a "Road Closed" sign only in Zone A. Once the fish swim away, the sign comes down, and boats can go back to fishing.

The Experiment: Testing the Two Systems

The researchers didn't just guess; they built a giant computer simulation (a "Management Strategy Evaluation") to test how these two systems would work over time. They treated the fish population like a bank account and the fishing boats like people trying to withdraw money.

Here is what they found, explained through simple analogies:

1. The "Smart Traffic" Wins on Efficiency

• The Static System (STC): To catch the same amount of "fishing pressure" (mortality) off the fish, the static system had to close a massive area for a long time. It was like closing the entire highway for a week just to stop a few cars.
• The Real-Time System (RTC): The dynamic system achieved the same protection by closing much smaller areas for much shorter times. It was like closing just one lane for 10 minutes.
• The Result: The RTC was like a surgeon's scalpel—precise and minimal. The STC was like a sledgehammer—blunt and disruptive.

2. The Money Factor (Economics)
You might think closing the ocean makes fishermen poorer. Surprisingly, the study found the opposite for the RTC system.

• Because the RTCs were so precise, they often closed areas where the fish were young and small (juveniles). By letting these young fish grow a little bigger before they could be caught, the fishermen ended up catching fewer fish, but bigger, more valuable fish.
• It's like a farmer who decides not to pick the tiny, unripe apples today. He waits a few weeks, and when he finally picks them, they are huge and sell for a premium price. The static system didn't give the fish this "growth time" because it was too broad and didn't target the right spots.

3. The "Data Recipe"
To make the "Smart Traffic" system work, you need the right ingredients (data). The researchers tested different ways of gathering this data:

• How far back should we look? Should we use data from last year, last month, or last week?
  • The Finding: Looking back one month was the sweet spot. It gave enough time to plan without the data becoming outdated. Looking back a whole year was too old, and looking back two weeks was too rushed to organize the boats.
• How do we know when to close? They tested two "alarm levels" (thresholds).
  • The Finding: They should set the alarm high (only close if the fish density is really high). This prevents closing areas unnecessarily.

The Big Takeaway

The paper concludes that for fast-moving, migratory fish like the Short Mackerel, flexibility is key.

• Old Way: "We will close this whole bay from March to May, no matter what."
• New Way: "We are watching the fish. If they gather in the north, we close the north. If they move south, we open the north and close the south. We only close the specific spot where the fish are, and only for as long as they are there."

This approach saves the fish population, reduces the "collateral damage" to the fishing industry, and actually helps fishermen make more money by catching better-quality fish. It turns fishing management from a rigid rulebook into a dynamic, responsive conversation with nature.

Technical Summary

1. Problem Statement

Fisheries management for highly migratory species, such as the short mackerel (Rastrelliger brachysoma) in the Gulf of Thailand (GOT), faces significant challenges. Traditional Static Time-Area Closures (STCs) use fixed boundaries and predetermined periods (e.g., seasonal bans) to protect spawning stocks. However, STCs often fail to protect migratory species because fish distributions shift dynamically in response to environmental and biological factors, leading to a mismatch between closure locations and actual fish "hotspots."

Conversely, Real-Time Closures (RTCs) offer a dynamic alternative that targets specific high-abundance zones based on current data. While promising, RTCs require rapid data collection and analysis, and their implementation involves complex trade-offs regarding:

• The temporal window of data used to identify hotspots (e.g., 2 weeks vs. 1 month vs. 1 year prior).
• The statistical threshold (e.g., standard deviations of CPUE) used to trigger a closure.
• The target level of fishing mortality reduction.
• The economic impact on fishers versus conservation benefits.

There is a lack of empirical frameworks to evaluate which RTC design rules optimize conservation goals without excessive economic loss, particularly in data-limited or effort-controlled fisheries.

2. Methodology

The study employed a Management Strategy Evaluation (MSE) framework to simulate and compare the performance of STCs against various RTC scenarios.

A. Operating Model

The simulation integrated two main components:

1. Catch Component: Used Random Forest Regression (RFR) to estimate spatiotemporal catch distributions. Since historical data contained gaps due to existing closures, the model predicted catch (kg) in unsampled grid cells (0.03° × 0.03°) using environmental covariates: Sea Surface Temperature (SST), Chlorophyll-a, sea depth, distance from shore, and latitude/longitude.
2. Fleet Dynamics Component: Modeled fishing effort based on actual Vessel Monitoring System (VMS) data and landing reports. Effort was also predicted using RFR for data-limited areas.
3. CPUE Calculation: Catch Per Unit Effort (CPUE) was calculated weekly for specific management units to serve as the primary indicator of fish abundance.

B. Management Scenarios

The study evaluated 1 Static Closure (STC) scenario and 18 Real-Time Closure (RTC) scenarios, varying three key parameters:

• Data Collection Window (Temporal Range):
  • Triggered Area: Data from the same month in the previous year.
  • Monthly: Data from 1 month prior.
  • Biweekly: Data from 2 weeks prior.
• CPUE Threshold (Hotspot Definition):
  • 3SD: Units exceeding 3 standard deviations above the annual mean CPUE.
  • 4SD: Units exceeding 4 standard deviations (more restrictive).
  • Fallback Rule: If no unit met the threshold, the unit with the highest CPUE was closed.
• Mortality Reduction Targets: 5%, 10%, 20%, and 30% reduction in fishing mortality.

C. Performance Evaluation

The MSE assessed three domains:

1. Spatial/Temporal Efficiency: Cumulative closure area and duration required to achieve targets.
2. Conservation Efficacy: Actual reduction in fishing mortality ($F$).
3. Economic Impact: Modeled using an exponential decay population model and von Bertalanffy growth equations. Income was estimated based on length-frequency data, converting fish length to weight and applying market prices.

3. Key Contributions

• Framework for Dynamic Management: Developed a robust MSE framework specifically tailored for migratory pelagic species, integrating VMS, landing reports, and environmental data to simulate RTCs.
• Optimization of Data Windows: Systematically tested the lag time between data collection and closure implementation, addressing a critical gap in RTC literature regarding the "information gathering period."
• Threshold Sensitivity Analysis: Provided empirical evidence on how different statistical thresholds (3SD vs. 4SD) affect the trade-off between closure size and mortality reduction.
• Economic-Conservation Trade-off: Demonstrated that dynamic closures can simultaneously reduce fishing mortality and increase post-closure fisher income by allowing fish to grow to optimal sizes before harvest.

4. Key Results

• Efficiency of RTCs vs. STCs:
  • RTCs achieved equivalent or greater fishing mortality reductions than STCs while requiring significantly smaller closure areas and shorter durations.
  • STCs required large, fixed areas to achieve a 5–10% mortality reduction, whereas RTCs achieved similar results with targeted, smaller zones.
• Optimal Data Window:
  • The study found no statistically significant difference in CPUE accuracy between using 2-week, 1-month, or 1-year historical data.
  • Recommendation: A 1-month data collection window is optimal as it balances administrative workload with sufficient accuracy for hotspot identification.
• Optimal Threshold:
  • The 4SD threshold was more efficient than 3SD. It required less closure area and time to achieve the same mortality reduction, though it resulted in slightly lower immediate revenue per unit area compared to 3SD in low-mortality scenarios.
• Economic Outcomes:
  • Income Increase: Unlike STCs, which often resulted in lower post-closure income due to the loss of mature fish and natural mortality during long closures, RTCs led to higher income after reopening.
  • Mechanism: RTCs often targeted areas with high juvenile abundance. Closing these areas allowed fish to grow to larger, more valuable sizes (increasing weight) before the fishery reopened, offsetting the temporary loss of catch volume.
• Mortality Reduction:
  • RTCs consistently outperformed STCs in reducing fishing mortality per unit of closure area.
  • However, actual mortality reductions were sometimes lower than targets if fish moved out of the closed area or if CPUE dropped to zero during the closure period.

5. Significance and Implications

• Policy Recommendation: The study validates RTCs as a superior management tool for migratory species in the Gulf of Thailand. It recommends implementing RTCs with a monthly data update cycle and a 4SD CPUE threshold.
• Operational Feasibility: The results suggest that existing surveillance infrastructure (VMS, electronic logbooks, landing reports) in Thailand is sufficient to support RTCs without requiring new, costly technologies.
• Stakeholder Engagement: The finding that RTCs can increase fisher income (by protecting juveniles that grow into larger, more valuable fish) provides a strong economic argument for fisher compliance, potentially overcoming resistance to closures.
• Future Directions: The authors highlight the need to integrate age-structure data and multi-species dynamics into future RTC designs to further refine conservation outcomes and minimize bycatch of non-target species.

In conclusion, this study demonstrates that shifting from static, broad-scale closures to dynamic, data-driven Real-Time Closures can effectively minimize fishing mortality for migratory short mackerel while enhancing the economic sustainability of the fishery.