The CPE of Channel Catfish is 9.6 fish per hoop net per night (=\(\frac{67}{7}\)).
The CPE of Cisco is 25.9 fish per 100 m of gillnet per night (=\(\frac{337}{650 \times 2} \times 100\)).
The CPE of age-1 Yellow Perch is 76.8 fish per 10 mins of trawling (=\(\frac{146}{7+6+6} \times 10\)).
Problems with CPUE Data
The conceptual relationship between CPUE and N is \(\frac{C_{t}}{f_{t}}=qN_{t}\) where \(C_{t}\) is the catch at time t, \(f_{t}\) (or \(E_{t}\) in the reading) is the effort expended at time t, \(N_{t}\) is the population abundance at time t, and \(q\) is the catchability coefficient. Note then that \(\frac{C_{t}}{f_{t}}\) is the catch per unit of effort. This relationship is a line with a slope of \(q\) and a y-intercept of 0.
The five main problems with CPUE data as identified by Maunder et al. (2006) are described below.
“Efficiency of a fleet” – catchability may increase over time as the fleet becomes more efficient due to new and better gear or learning more about how to catch the stock of fish.
“Targeting by a fleet” – catchability may change if the fishery switches target species; i.e., catchability for the previously targeted species will decline and catchability for the newly targeted species will increase.
“Environmental factors” – Environmental factors such as changes in temperature, wind patterns, or currents may affect catchability.
“Dynamics of the fleet” – Changes in the dynamics of the fishery may cause the relationship between CPUE and N to be nonlinear. For example, if fishers expand their spatial range then this may impact the CPUE-abundance relationship.
“Management influence” – Management decisions can affect the CPUE-abundance relationship. For example, closing seasons or restricting effort allocations when quotas are nearly met will impact catchability.