Spatial and temporal effects improve Bayesian price estimation for the small-scale shrimp fishery in Sergipe State, Brazil

Local shrimp productivity and economic infrastructure may vary among small fishery communities, which can lead to unequal conditions for fishers. Consequently, the domestic market value for shrimp landing can be different and decentralized in space and time. Small-scale fisheries (SSF) have several characteristics, including the spatial and temporal interactions, which add uncertainty to fishery statistics. Bayesian hierarchical models allow parameters to vary on several levels using random effects or other means of randomization, which leads to better estimation of multilevel uncertainty compared to other methods. This study tests the influence of space and time on shrimp production prices in Sergipe State, Brazil, using a Bayesian hierarchical modelling approach. We also tested whether there is a relationship between capture per unit effort and production value. We described landings of Litopenaeus schmitti and Xiphopenaeus kroyeri in 27 locations in the State of Sergipe between 2010 and 2016. Shrimp production (kg) remained relatively stable (200–300 ton/year). Using the Bayesian approach, we found that prices of both species varied among landing points within and between years, and this variation has spatial and temporal dependence. The model enhanced our understanding of which factors affect price variability in landings. In particular, our models indicated that catch per unit of effort, location, and time affected the price variability in coastal landings within the State of Sergipe. However, the seasonal monthly effects were not as important as the yearly effects, since the variance within years was found to be low. This could indicate that economic activities (tourism) do not play an important role for shrimp prices in this region. Yet, biological factors (abundance and reproduction period) can affect the prices for some locations. Improving the estimates by using methods that can account for the human dimension of fishing activities is paramount in its management, enhancing the decision-making process.