Examine historical and current billfish landings and catch rates
Quantitative, qualitative, and ethnographic methods will be used to collect data on billfishes. Data components will include fishery type (artisanal, recreational/sport and industrial), catches, biological and ecological parameters, composition and weight, fishing effort, size, gears used and fishing locations of capture/fishing, target species, and bycatch species. Relevant biological and ecological (size, sex, gravidity; maturity of the species) data will be collected at landing sites, at sea. For historical trends, secondary data sources and databases (FAO, IOTC, ORI, IIP, National Fisheries Institutions, and private tagging databases) will be used. Fishery dependent and independent data will be collated, followed by data analysis, identification of information gaps and development of recommendations.
Predictive models fitted to landings data will be used to determine the influence of several predictor variables (e.g. seasonality, time, gear, distanced fished, boat size, fishery type, size of fish among others). In addition, environmental variables such as currents, sea surface temperatures (SST) and wind will also be considered in the predictive models to determine the effect on the catch or landings rate.
We will consider tourism activity as one of the predictor variables in our models. One way to get the data on tourism activity would be to include this variable in the socio-economic analysis instrument or creel surveys. In terms of measurements to operationalize tourism, the number of sport fishing tournaments, the number of anglers/fishers and boats fishing in a given area during periods of low and high fishing activity, and, as well as different lengths of stay, expenditure patterns, impacts on nature, and interactions with local inhabitants will be used. The Local Economic Benefit (LEB) from tourism can be calculated by estimating the annual number of tourist days in each tourist category, multiplying it with tourists’ daily expenditures, and calculating how this translates into direct and indirect local value-creation.
Collection of effort data will enable us to understand patterns in catch as well as providing robust estimates of CPUE. Effort data will include both positive and zero landings or catches. The Global Fish Watch dataset which is a freely available data on Google Cloud will be used to mine for information on fishing effort (number of hours fishing) as observed by satellites. A full stock assessment will not be undertaken within the scope of this project; however, it will be possible to compare the project CPUE data with the historical data and thus derive abundance indices. The three- year collection of CPUE data will be used to apply length-based methods to calculate life-history parameters and generate metrics of abundance. A meta analyses will be conducted using various datasets (e.g., Reg Watson et al. data hosted at the University of Tasmania; IOTC; The Sea Around Us and Global Fishing Watch among others).
Activity will be carried out across the WIO including project partner countries and others that are willing to provide data.
Commence at the start of the project and continue for 12 months.
Team leaders Dr. Nelly Isigi Kadagi and Dr. Nina Wambiji and assisted by all