Crossposting from the Reconhub:
Together with my co-author Stephen Stohs, I recently published an article in the American Journal of Agricultural Economics. The main gist is that with rare events like endangered species interactions, the statistical information in yearly data sets is limited, and that data from several years provide better information for decision making. We provide a method that is based on the Kalman filter and that allow for observations unequally spaced in time. The method also takes account of spatiotemporal effects. We discuss the particular case of leatherback turtle bycatch in a gillnet fishery in California and Oregon. The leatherback is an endangered species, and in order to reduce bycatch, extensive spatiotemporal closures was imposed on the fishery in 2000. Our analysis shows that the interaction risk likely was smaller than in the scenarios that motivated the closures. To discuss whether the closures were and are warranted, require further analysis, however. As we discuss in the concluding section, closures in California may lead to trade leakages such that the total effect on the leatherback turtle stock is unknown. And the value of the leatherback in the ecosystem, and the value of its mere existence, is unknown.
To address the tradeoff between biodiversity conservation in marine ecosystems and fishing opportunity, it is important to quantify the risk of endangered species interactions in commercial fisheries. We propose a Kalman filter suitable for rare events to estimate the endangered leatherback turtle take risk in the California drift gillnet fishery in the years 1990–2010, conditional on spatiotemporal factors that affect take rates. Results suggest interaction risk has remained stable, but with substantial variation over the spatiotemporal distribution of effort. Our methods might also apply to recreation demand analysis with rare event risk, or to applications involving irregularly spaced observations, like trade-level stock market data.