Modelling Cooperation between fishermen with a Cellular automaton. A framework for fishing effort spatial dynamics

Dreyfus-León, Michel

doi:10.1016/j.ecoinf.2005.08.002

Cited by 7 publications

(15 citation statements)

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“…Within cooperative agreements, the value of these data collection programs is maximized by a dynamic flow of spatiotemporal information among users, where cooperative members often have less or no incentive to compete with one another for sub-stocks with high fishery yields and economic value (Deacon et al 2010). Thus, coordinated harvesting typical of well-structured fishing cooperatives has the potential to alleviate the "race for fish" often associated with other forms of traditional management and optimize the extraction of fishery resources improving yields and revenues (Dreyfus-Leon 2006, Costello and Deacon 2007, Ovando et al 2012.…”

Section: Introductionmentioning

confidence: 99%

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Exploring benefits of spatial cooperative harvesting in a sea urchin fishery: an agent‐based approach

et al. 2017

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Citation: Gutierrez, N. L., P. Halmay, R. Hilborn, A. E. Punt, and S. Schroeter. 2017. Exploring benefits of spatial cooperative harvesting in a sea urchin fishery: an agent-based approach. Ecosphere 8(7):e01829. 10.1002/ecs2.1829Abstract. Sedentary or low-mobility organisms show a high degree of dependency with their substrate, where its heterogeneity often determines small-scale spatial patterns of distribution, life history traits, and fishery yields. For sea urchins, this spatial structure is usually shaped by food availability, habitat structure, individual movement, and fishery dynamics. All of these have a significant impact on their physiological and reproductive status and in particular on their gonadal content. These patterns are of particular interest considering that the sea urchin fishery is a roe fishery where marketability depends on gonad yield and quality, which in turn is related to spatial and temporal variations in associated kelp beds. Thus, better gonad quality and yields generate higher profits for both fishers (divers) and processors. However, competition among divers within a non-cooperative system creates a "race for shellfish" precluding higher gonad yields per unit of effort. A spatially explicit agent-based model for the San Diego, California red sea urchin (Strongylocentrotus franciscanus) fishery was developed in order to assess the benefits of cooperative harvesting by depicting spatial and temporal variations in fishery yields. A cooperative harvesting scenario where divers consistently target those areas with higher yields avoiding low-quality sea urchins was compared against a non-cooperative situation where divers harvest at random or based only on densities of sea urchins. Sea urchin population at the end of the simulation period was 20% higher for the most cooperative scenario compared to the non-cooperative fishery. Further, for the most cooperative scenario where information sharing among divers is greatest and harvest is coordinated, sea urchin catches were at least 10% higher and gonad yield 35% higher than in the non-cooperative scenario. In this model, information sharing and organized harvesting typical of well-functioning cooperatives allowed fishers to optimize the use of the resource in terms of higher gonad yields per unit of effort while maintaining the productivity of the stock. This study also highlights the importance of community-based management (i.e., collaborative efforts in assessment, management, and governance of fisheries between fishers, scientists, and managers) toward improving fisheries sustainability.

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Section: Introductionmentioning

confidence: 99%

“…). Thus, coordinated harvesting typical of well‐structured fishing cooperatives has the potential to alleviate the “race for fish” often associated with other forms of traditional management and optimize the extraction of fishery resources improving yields and revenues (Dreyfus‐Leon , Costello and Deacon , Ovando et al. ).…”

Section: Introductionmentioning

confidence: 99%

Exploring benefits of spatial cooperative harvesting in a sea urchin fishery: an agent‐based approach

et al. 2017

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“…For example, the flow or exchange of information relative to the quality of the fishing grounds can influence the group or other groups of fishers' decision regarding where to direct their effort (Palmer 1991, Little et al 2004. This aspect of the fishers' behavior is rarely analyzed or modelled (Dreyfus-León 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Por ejemplo, el flujo o intercambio de información con respecto a la calidad de las zonas de pesca puede llegar a influenciar al grupo u otros grupos de pescadores en la toma de decisiones de hacia donde dirigir su esfuerzo (Palmer 1991, Little et al 2004. Este aspecto del comportamiento del pescador raramente es analizada o modelada (Dreyfus-León 2006). emphasis on modeling a resource (Dreyfus-León 2006).…”

Section: Introductionunclassified

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Numeric simulation of fishing effort and strategies (stochastic and cartesian) using cellular automata

Dreyfus-León¹,

Martinez-Olvera

Hernández-Walls

2011

Cienc. Mar.

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A cellular automaton (CA) model is developed to analyze the behavior of fishermen in terms of belonging to a group that exchanges information on fishing and the personal aspect of decision making, defining the fishermen as cartesian or stochastic. This model aims to be the generic structure for a subsequent specific model suitable for a real fishery, and shows how the previously described behavior can be represented in a CA. The results show that, in a simulated world, positive effects are observed in terms of capture or rate of capture, the grouping, and the stochastic behavior in the event of resource scarcity. Also shown is how the behavior of including an explorer has the potential of generating benefits, as well as being risky. If the fishing is good, an independent boat benefits more than the group explorer when sharing this information with the other members of the group (cooperation-competition effect); however, in an adverse situation, the group explorer is not as affected as the independent boat.

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Recknagel¹

2006

Ecological Informatics

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Modelling Cooperation between fishermen with a Cellular automaton. A framework for fishing effort spatial dynamics

Cited by 7 publications

References 7 publications

Exploring benefits of spatial cooperative harvesting in a sea urchin fishery: an agent‐based approach

Exploring benefits of spatial cooperative harvesting in a sea urchin fishery: an agent‐based approach

Numeric simulation of fishing effort and strategies (stochastic and cartesian) using cellular automata

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