Science: Fishing success not dependent on skill level

Catch inequality is a term that describes the phenomenon of a small number of anglers catching a disproportionately large number of fish.  It happens commonly in recreational fisheries but it has never been the subject of a long-term study, and not much is known about how and why it changes over time.  There have been studies that suggest catch inequality is related to catch per unit effort (CPUE).  Generally, there is low catch inequality when CPUE is high and high catch inequality when CPUE is low.  Catch inequality, therefore, reduces the usefulness of using CPUE as an indicator of fishing success and could reduce the effectiveness of management decisions.  Seekell et al. (2011) tested the hypothesis that catch inequality increases as CPUE decreases because of resource scarcity.

            Seekell et al. (2011) analyzed 20 years (1988-2007) of catch and effort collected from a brown trout fishery on a section of Wappinger Creek that flows through the Cary Institute of Ecosystem Studies (IES) in Millbrook, New York.  Anglers had to obtain a permit from IES to fish the area.  All anglers were required to attend an orientation session and submit angling log sheets to obtain the permit.  Catch inequality, both between anglers and between trips, was evaluated by plotting Lorenz curves, a plot of the cumulative share of fish caught by the cumulative share of effort.  The Lorenz curve was used to calculate the Gini coefficient, a number between zero and one with zero representing complete equality and one representing maximal inequality.  CPUE was calculated for each year but adjusted by balancing inequality and mean catch rate with the Sheshinski-Sen-Yitzhaki index (SSY).  They also tested for a trend by calculating the Pearson’s product-moment correlation coefficient between mean trip length, trip length variance, number of trips per season, total effort, the Gini coefficient and year.

            Catch inequality, overall, increased significantly during the 20 years of the study.  However, catch inequality between trips increased significantly while there was no change in catch inequality between anglers.  CPUE and SSY decreased significantly over the course of the study.  There was a strong relationship between CPUE and the Gini coefficient, indicating a strong relationship between CPUE and catch inequality.  Total effort, total number of trips, mean trip length and trip length variance decreased significantly and, with the exception of trip length variance, were inversely correlated to the Gini coefficient.

            The decline in CPUE and SSY signify that fishing quality declined over the course of the study, regardless of the effects of catch inequality.  The decline in CPUE was due to increasing inequality between trips not inequality between anglers.  The study did not provide a mechanistic or theoretical basis for these trends in angler success.  Analyses of commercial fishing suggest that skill level and environmental stochasticity can explain variability in success.  The increasing catch inequality between trips signifies increasing environmental stochasticity, and the constant catch inequality between anglers suggests little or no change in the skill of the anglers as a whole.  Since changes in CPUE can be due to changes in the fish population, a form of environmental stochasticity, and skill of the anglers, the constant catch inequality between anglers suggests that the changes in the CPUE were due to changes in the fish population.  Therefore, this study suggests that calculating CPUE from angler logs can provide good estimates of fishing quality.

Implications for anglers

            This study demonstrates that, while it is important to be a skillful angler, picking the right environmental conditions to go fishing is more important.  An inferior angler can catch a fish when the conditions are good but the best angler can’t catch a fish on a bad day.  So continue to improve your casting, hook setting, lure selection and other techniques, but you’ll probably see a better catch rates by keeping a record of your catches and environmental conditions.  Go out when environmental conditions are optimal and avoid days that don’t look so good, but I know it’s hard to resist the urge to do some casting even if you know you probably won’t catch anything.

            Be wary of using CPUE as an indicator of fishing quality.  The CPUE calculation in this study was good but other methods may not be.  CPUE is an index of abundance and is not necessarily related to fishing quality.

            When reading about fisheries studies don’t get bogged down by statistics and associated terms.  The calculations for statistical tests are complex but the results are usually explained and easy to understand.  Fisheries scientists have to include the statistics so readers can verify that the methods satisfy the hypotheses being tested, so methods can be reproduced and to appease egghead mathematicians in the audience.  The statistics are usually not important to understanding and interpreting the results of a study, so don’t let them prevent you from reading a report from study.

Selected definitions

Variance:  the measure of how far each value in a data set is from the mean

Catch inequality: the phenomenon of a small number of anglers catching a disproportionately large number of fish

Environmental stochasticity: unpredictable spatiotemporal fluctuations in environmental conditions

Seekell, D.A., C.J. Brosseau, T.J. Cline, R.J. Winchcombe and L.J. Zinn. 2011. Long-term changes in recreational catch inequality in a trout stream. North American journal of fisheries management. 31: 1100-1105