Traditional methods of creating marine cost layers have included mapping current use by surveys of fishing activities. This includes either mapping catch per unit effort of one industry such as Stewart et al (2003) with rock lobster or mapping current fleet use such as Richardson et al (2006). Cost layers created to date have typically been biased to a single fishing sector and may not give ample information on the tradeoffs faced by the different stakeholders groups (i.e. trawling vs net fishery). When creating current use maps there are two other issues. One is that fisherman may not trust interviewers or research agency and therefore not disclose actual fishing regions. The other issue is that there may be potential areas for fishing such that socio-economic and conservation objectives could both be met by shifting efforts spatially. To identify these areas mapping current fishing effort only is not adequate. We have examined alternate methods for creating marine cost layers as well as validating current methods. To do this we used catch per unit effort surveys to identify key species for consumption and revenue. We then modeled the presence/absence data of these species based on predictor variables such as habitat, bathymetry, distance from land, exposure to waves and tides, and protection status. For each species we examined the best fit with four different predictive models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial. These output predictive maps were then combined with catch percentage, market values, and gear types (i.e. distance travelled, fuel usage of different boats) to create cost layers. We used data from catch per unit effort surveys to validate the information given from opportunity cost and foregone revenue models. This project has helped us to better understand how cost information can be used to reduce socio-economic impacts of marine protected areas.
We used Marxan software to explore options for design and reconfiguration of a cost-effective MPA network for Kubulau District's traditional fisheries management areas (qoliqoli) that met the conservation targets for all reef types. The conservation target of 30% was based on the Fijian Government’s declaration at the Barbados Plan of Action in Mauritius in 2005 to protect 30% of its inshore waters. We selected CPUE as a cost measure to reflect current fishing effort. We ran Marxan using four different scenarios:
(1) We used catch per unit effort (CPUE) as the cost layer and did not include current protected areas (clean slate CPUE);
(2) We used CPUE as the cost layer and required that current protected areas were included in the reconfigured
MPA network (locked in CPUE);
(3) We used opportunity cost as the cost layer and did
not include current protected areas (clean slate Opp); and
(4) We used opportunity cost as the cost layer and
required that current protected areas were included in the reconfigured MPA network (locked in Opp).
Outputs from this study will be combined with outputs of two simultaneous studies to:
(1) Develop improved predictive maps of fish asssemblage characteristics (biomass, species richness, diversity) from satellite-derived habitat variables; and
(2) Spatial metrics of reef resilience derived from predictor variables (e.g. herbivorous fish biomass, coral population structure and recruitment, benthic cover, slope and aspect, flushing, shading, reef topography).
These data layers will be combined into several Marxan trials to produce output recommendations to present to key community stakeholders for alternative ways to reconfigure the Kubulau MPA network. WCS will present several output maps to the Kubulau Resource Management Committee (KRMC), with justification for how they optimize opportunity cost, fisheries benefits and reef resilience. The KRMC will then consult with the high council of chiefs for the district to determine if any changes will be made to the existing network.
Outputs from this study will be combined with outputs of two simultaneous studies to:
(1) Develop improved predictive maps of fish asssemblage characteristics (biomass, species richness, diversity) from satellite-derived habitat variables; and
(2) Spatial metrics of reef resilience derived from predictor variables (e.g. herbivorous fish biomass, coral population structure and recruitment, benthic cover, slope and aspect, flushing, shading, reef topography).
These data layers will be combined into several Marxan trials to produce output recommendations to present to key community stakeholders for alternative ways to reconfigure the Kubulau MPA network. WCS will present several output maps to the Kubulau Resource Management Committee (KRMC), with justification for how they optimize opportunity cost, fisheries benefits and reef resilience. The KRMC will then consult with the high council of chiefs for the district to determine if any changes will be made to the existing network.
Adams VM, Mills M, Jupiter SD, Pressey RL (2010) Improving social acceptability of marine protected area networks: a method for estimating opportunity costs to multiple gear types in both fished and currently unfished areas. Biological Conservation doi:10.1016/j.biocon.2010.1009.1012
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