Understanding socio-ecological drivers of spatial allocation choice in a multi-species artisanal fishery: A Bayesian network modeling approach |
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| Affiliation: | 1. Consultant Services Department, ALNA S.A., PO Box 11401, San José, Costa Rica;2. CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania 7001, Australia;3. Centre for Marine Socioecology, University of Tasmania, Hobart 7000, Australia;4. Chief Economist Unit, DG Trade, European Commission, Charlemagne Building, Brussels 1040, Belgium;1. Institute for Environmental Protection and Research (ISPRA), Loc. Brondolo, 30015 Chioggia, Italy;4. National Research Council (CNR), Institute for Marine Sciences (ISMAR), Largo Fiera della Pesca, 60125 Ancona, Italy;1. Fisheries Ecosystems Laboratory (LabPesq), Oceanographic Institute, University of São Paulo, Praça do Oceanográfico 191, Cidade Universitária, São Paulo, SP, Brazil;2. Graduate Program in Oceanography, University of São Paulo, Brazil;1. National Marine Science Centre, Southern Cross University, PO Box 4321, Coffs Harbour NSW 2450, Australia;2. Ministry of Agriculture & Food, Forests and Fisheries, PO Box 871, Nuku’alofa, Tonga;3. Ministry of Fisheries & Marine Resources Development, PO Box 64, Bairiki, Tarawa, Kiribati;4. Partners in Community Development Fiji, 8 Denison Rd, Suva, Fiji;1. Fisheries Department, Faculty of Marine and Atmospheric Sciences, Hormozgan University, Iran;2. Norwegian College of Fishery Science, University of Tromsø, 9037, Tromsø, Norway;3. Fisheries Department, Gorgan University of Agricultural Sciences and Natural Resources, Iran;1. Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Km 6 Antigua Carretera a Progreso, Cordemex, CP 97310, Mérida, Yucatán, Mexico;2. Universidad Marista de Mérida. Periférico Norte Tablaje Catastral 13941, Carretera Mérida, Progreso, C. P. 97300, Mérida, Yucatán, Mexico;3. Escuela Nacional de Estudios Superiores, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz, Km 4, Ucú, Yucatán, 97357, Mexico |
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| Abstract: | Effective management of artisanal fisheries requires understanding fishers, their behaviors, and the drivers that underpin their choices. Behavioral drivers are critical links in understanding the interactions between social and ecological systems and can help inform effective management approaches. A Bayesian Belief Network modeling approach was used to investigate a diverse range of qualitative and quantitative social and ecological drivers of spatial location choice in a multi-species artisanal dive fishery in Costa Rica. Empirical and observer data used to populate the BBN showed the influence of economic factors, environmental conditions as well as social interactions on the decision-making process of spatial location choice. Good governance scenarios represented by Responsible Fisheries Marine Areas Management were analyzed for both hookah and free diving methods to assess the effects of responsible fishing on the fishers and the fishery. Model based-scenario analysis suggests that management interventions should consider the fisher's potential behavioral responses in the context of environmental variability, dependence on cultural assets, and food security. The results show that there is a need to understand fisher's decisions based on broad socio-ecological system understanding and consider the environmental outcomes alongside food security and the cultural significance of different marine species to fishing communities. |
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| Keywords: | Bayesian network modeling Fishers behavior drivers Fishing effort allocation Multi-species artisanal fishery Responsible Fisheries Marine Area Management Socio-ecological fisheries systems |
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