Is catch-per-unit-effort proportional to abundance?

Abstract

We compiled 297 series of catch-per-unit-effort (CPUE) and independent abundance data (as estimated by research trawl surveys) and used observation error and random effects models to test the hypothesis that CPUE is proportional to true abundance. We used a power curve, for which we were interested in the shape parameter (β). There was little difference among species, ages, or gear types in the distributions of the raw estimates of β for each CPUE series. We examined three groups: cod, flatfish, and gadiformes, finding strong evidence that CPUE was most likely to remain high while abundance declines (i.e., hyperstability, where β < 1). The range in the mean of the random effects distribution for β was quite small, 0.64–0.75. Cod showed the least hyperstability, but still, 76% of the mass of the random effects distribution was below 1. Based on simulations, our estimates of β are positively biased by approximately 10%; this should be considered in the application of our findings here. We also considered the precision of CPUE indices through a meta-analysis of observation error variances. The most precise indices were those from flatfish (median coefficient of variation of [Formula: see text]0.42).

Assessing Changes in Fisheries Using Fishers’ Knowledge to Generate Long Time Series of Catch Rates: a Case Study from the Red Sea

“…The most basic and informative data in fisheries are time series of catch and effort (Caddy andGulland 1983, Pauly andZeller 2003), from which catch per unit effort (CPUE) is calculated, which can be, when used with caution, an indicator of abundance (Harley et al 2001). This is why fishery data collection is critical at both local and international levels (Garibaldi 2012).…”

Application of a multi-annual generalized depletion model to the assessment of a data-limited coastal fishery in the western Mediterranean

“…The values of the b parameter calculated (0.615 for trammel net and 0.777 for bottom trawl) indicate hyperstability of striped red mullet, i.e. the catches per unit effort are stable over time while the abundance of the stock is actually decreasing (Harley et al 2001), indicating that perceived trends in the CPUE series are not indicative of the real stock status. The application of MAGD to red mullet and cuttlefish showed that the model could not capture the existence of months with high catches that appear as outliers in the models (qq-plots in the lower right panels of Figs 6 and 7).…”

Harvest Regulations and Implementation Uncertainty in Small Game Harvest Management

“…From our perspective we need both empirical and simulation studies to start filling this gap. First, inspired by the fisheries literature (e.g., Harley et al, 2001) we need large-scale and cross species examinations of know how often, and under which conditions, density dependent catchability coefficients (i.e., hyperstability) are most likely to arise. In these cases, we suspect that strict regulations are particularly important at low population densities.…”

“…Interestingly, bag limits are often criticized because they lack precision in the ability to control harvest rates, even if the bag limit changes in response to population size (Andersen, 2015). In addition, concerns have been raised because the catchability coefficient seem to be inversely density dependent (Andersen and Kaltenborn, 2013;Eriksen et al, 2017) creating a risk that harvest rates become higher at low population densities (se e.g., from the fishery literature; Harley et al, 2001;Ward et al, 2013;van Poorten et al, 2016). It has been indicated that managers can achieve more control of offtake by allocating seasonal quotas to individual hunters or hunting teams (see e.g., Gibson et al, 2011), where the hunters/teams are allocated a given proportion of total area quota (see e.g., Kurki and Putaala, 2010).…”