2016
DOI: 10.1371/journal.pone.0159626
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Modeling the Spatial Dynamics of International Tuna Fleets

Abstract: We developed an iterative sequential random utility model to investigate the social and environmental determinants of the spatiotemporal decision process of tuna purse-seine fishery fishing effort in the eastern Pacific Ocean. Operations of the fishing gear mark checkpoints in a continuous complex decision-making process. Individual fisher behavior is modeled by identifying diversified choices over decision-space for an entire fishing trip, which allows inclusion of prior and current vessel locations and condi… Show more

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Cited by 8 publications
(8 citation statements)
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“…Most work on fleet dynamics to date indicates that spatial patterns in fishing effort are related to catch, economic costs, and social factors [ 65 – 67 ]. The few studies that have addressed the effects of dynamic oceanographic factors on spatial patterns in fishing activity have mostly focused on pelagic fisheries (e.g., tuna) and environmental conditions in surface waters (e.g., temperature, chlorophyll) at ocean basin scales [ 68 71 ]. In these studies, the importance of environmental factors varies among fisheries and with the spatial scale of investigation, but in some cases are of similar importance to economic and social factors [ 71 ].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Most work on fleet dynamics to date indicates that spatial patterns in fishing effort are related to catch, economic costs, and social factors [ 65 – 67 ]. The few studies that have addressed the effects of dynamic oceanographic factors on spatial patterns in fishing activity have mostly focused on pelagic fisheries (e.g., tuna) and environmental conditions in surface waters (e.g., temperature, chlorophyll) at ocean basin scales [ 68 71 ]. In these studies, the importance of environmental factors varies among fisheries and with the spatial scale of investigation, but in some cases are of similar importance to economic and social factors [ 71 ].…”
Section: Discussionmentioning
confidence: 99%
“…The few studies that have addressed the effects of dynamic oceanographic factors on spatial patterns in fishing activity have mostly focused on pelagic fisheries (e.g., tuna) and environmental conditions in surface waters (e.g., temperature, chlorophyll) at ocean basin scales [ 68 71 ]. In these studies, the importance of environmental factors varies among fisheries and with the spatial scale of investigation, but in some cases are of similar importance to economic and social factors [ 71 ]. In economic models of fine-grained spatial fishing behavior, environmentally-driven effects are captured implicitly; recent past location-specific revenues and fishing location choices are predictors of current location choices [ 72 75 ].…”
Section: Discussionmentioning
confidence: 99%
“…The demand system itself is subject to several ex-ante decisions on the part of the analyst concerning the appropriate market delineation (the limits of the relevant market). Several recent studies have shown strong globalization of the tuna markets [3740] [33] and identified two separate market chains: purse-seine/cannery-grade and long-line/sashimi-grade tuna markets [41] [42] [9] [36]. Each of the two distinct markets, purse-seine/cannery-grade and long-line/sashimi-grade, are highly integrated at the global level by both price and commodity flows across locations and species, making any regional change in catches important to the entire industry.…”
Section: A General Synthetic Inverse Demand System Approachmentioning
confidence: 99%
“…The multidisciplinary approach makes all the more sense when the aim of the research concerns the mobility of fishing vessels in relation to the spatial distribution of exploited stocks. Discrete choice models are often applied to the spatial analysis of fisheries (Bockstael and Opaluch, 1983;Ward and Sutinen, 1994;Sun et al, 2016). The distribution of fishing effort in space depends on codified and tacit knowledge (Polanyi, 1966).…”
Section: Spatial Distribution Of Stocks and Fleet Mobilitymentioning
confidence: 99%