Effects of misrepresentative length samples on individual growth and stock condition estimates

Despite its importance in fisheries studies, there is insufficient understanding on the effect of sampling error or bias on individual growth and other stock indicators. We show the influence of sample length distributions on parameter estimates, illustrating with an example. For the brown swimming crab, we simulated length samples in five configurations and estimated parameters of von Bertalanffy (k,  , t0), asymptotic weight ( ), weight-length relationship (a, b), growth performance (01) and condition factor (Kn). Parameter estimates were compared with baseline values using relative bias, standard error and root mean square error. The results show that the accuracy and bias of parameter estimates depend on the lengths sampled. For example, the bias and accuracy of and vary inversely with sampled length, whereas combining length segments yields smaller biases of k and t0 than those of and . In general, the accuracy of parameter estimates does not always depend on sampling the entire length range, and errors are not the same for all parameters. These results are useful to guide sampling when resources are scarce. We discuss potential reasons for incomplete length sample structure and offer recommendations to obtain best estimates for parameters of interest.

Automated summary

Despite its importance in fisheries studies, there is insufficient understanding on the effect of sampling error or bias on individual growth and other stock indicators. We show how sample length distributions influence parameter estimates and provide recommendations for obtaining best estimates for parameters of interest. Our results demonstrate that the accuracy and bias of parameter estimates depend on the lengths sampled, and errors are not the same for all parameters.