Using censored regression when estimating abundance with cpue data to account for daily catch limits

Abstract

In fisheries where there is a limit on total catch in a given period, catch-per-unit-effort (CPUE) data may not be proportional to abundance because catches may be censored at the limit. Commonly used depletion estimators (e.g., Leslie method) could be biased when ordinary least squares (OLS) regression is used to estimate abundance with censored CPUE data. We used simulations to examine the performance of OLS regression and a censored regression approach when estimating abundance and exploitation using censored CPUE data over a range of known exploitation rates. We also applied the censored regression approach to data from a commercial fishery for the eastern oyster (Crassostrea virginica). The censored regression approach always performed better than the OLS regression when estimating abundance and exploitation in our simulations. Harvest and abundance of oysters in Fishing Bay, Maryland, increased during 2009 to 2013 and then decreased through 2016, while exploitation rates had no substantial trend over time. The censored regression approach is useful for estimating abundance and exploitation when the distribution of CPUE is affected by daily catch limits.