Investigating record linkage for combining voluntary catch reports with a probability sample

Incorporating multiple samples for estimation is becoming increasingly popular across industries and disciplines. Indeed, various fisheries agencies combine samples when estimating catch totals. In this paper, we examine a probabilistic matching technique to link a non-probability sample of electronic reports of fishing trips with a dockside probability sample. We use 2017 data from a capture-recapture pilot study in the Gulf of Mexico and compare it with current methodologies. We also examine a key parameter used in record linkage-a threshold score for the algorithm-and study its effect in this setting. We show that combining non-probability data with a probability sample is useful for estimating fish catch and argue record linkage is a useful way to link the data.

Automated summary

Incorporating multiple samples for estimation is increasingly popular, and this paper examines a probabilistic matching technique to link non-probability and probability samples. Using 2017 data, the study shows that combining non-probability data with a probability sample is useful for estimating fish catch, and record linkage is an effective way to link the data.