Validating matches of electronically reported fishing trips to investigate matching error

Abstract

AbstractObjectiveRecently, researchers have developed methods for combining probability samples and non‐probability samples. In recreational fisheries management, data from probability samples are typically counts of catch from a random sample of trips intercepted by a sampler, while non‐probability samples consist of catch data that are collected in self‐reports made to a fishery management agency. These reports are typically transmitted electronically and are known as an electronic logbook (ELB). Even when such reporting is mandated, compliance is not universal. Since the inclusion probability for any particular angler is unknown, the ELB sample is a non‐probability sample. We used data from a 2017 Gulf of Mexico (GoM) pilot study in which charter captains volunteered to electronically report their catch. At the dock, they could also be intercepted by a sampler, at which time their catch was observed. Estimates of total catch can be generated if trips from the two data sets can be accurately matched. Several states in the GoM implement similar ELB reporting augmented with a probability sample. However, there is an apparent discrepancy between National Oceanic and Atmospheric Administration (NOAA) estimates of the total and the ELB estimates for the same geographies. We seek to investigate the extent to which matching errors contribute to the discrepancies.MethodsWe employed probabilistic record linkage to match reports with intercepts and developed a validation tool to examine the matches. Using our validation tool, we examined several methods of estimating the total catch of Red Snapper Lutjanus campechanus in the GoM to investigate the potential cause of the discrepancy.ResultWe found the existing differences between the NOAA estimates and estimates resulting from combining ELB reports in this application were likely not due to matching error but instead were apparently derived from other sources of non‐sampling error.ConclusionThis has implications for new and existing ELB implementations, which are gaining popularity. The tool and results we provide can allow other implementations to better match reports with intercepts and offers a way to examine the extent to which matching errors affect the bias of estimates. Our work also shows that agencies should focus on non‐sampling errors besides matching error to reduce bias. Our tool may also be extended to examine such non‐sampling errors, such as the assumption that reporting and interception are independent.