Evaluating adaptive management frameworks for data-limited crustacean fisheries

Area of Science:

• Fisheries Science
• Marine Ecology
• Conservation Biology

Background:

• Crustacean fisheries are vital for global food security and economies, particularly in developing nations.
• Many Asian crustacean fisheries face challenges with limited data, scientific capacity, and effective management.
• Adaptive management frameworks offer potential solutions for data-limited fisheries by improving data collection and stock assessments.

Purpose of the Study:

• To evaluate the suitability of three adaptive fisheries management frameworks (FISHE, FishPath, DLMtool) for Asian crustacean fisheries.
• To identify data and modeling needs and management gaps within these fisheries.
• To propose an integrated framework tailored for crustacean fisheries management.

Main Methods:

• Applied three adaptive management frameworks (FISHE, FishPath, DLMtool) to diverse Asian crustacean fisheries.
• Contrasted framework applicability based on data availability, governance, and socio-economic contexts.
• Compared framework outputs to identify strengths, weaknesses, and data/modeling requirements.

Main Results:

• Each framework offered suitable management recommendations but had limitations; FISHE was more holistic, while FishPath and DLMtool focused on stock assessment and management strategy evaluation, respectively.
• Challenges in collecting commercial catch data due to funding and monitoring program design hindered management implementation.
• All frameworks faced difficulties with crustacean-specific life histories compared to finfish.

Conclusions:

• An integrated adaptive framework combining qualitative and quantitative approaches is proposed for crustacean fisheries.
• This integrated approach should consider unique crustacean life histories, climate change impacts, and participatory processes.
• Balancing socio-economic and ecological objectives is crucial for effective adaptive management in these fisheries.