Effect of different sampling designs and methods on the estimation of secondary production: A simulation

• Published in: Limnology and Oceanography: Methods
• Main Authors: Cusson, Mathieu, Plante, Jean‐François, Genest, Christian
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2006
• Subjects: Subarctic
• Online Access: http://dx.doi.org/10.4319/lom.2006.4.38; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.4319%2Flom.2006.4.38; https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2006.4.38

This article reports the results of a simulation study designed to investigate the effect of several sampling design factors on the accuracy and precision of various estimates of secondary production. Whereas most previous studies of this sort were concerned with freshwater fauna (e.g., insects), the hypothetical population used here reflects the characteristics of marine mussels from cold‐temperate and subarctic regions. It features the simultaneous presence of different cohorts, gradual recruit arrival, seasonal growth oscillation, and quadratdependent population density, as well as random individual variation both in survival and in weight gain. For this population, the percentage relative bias (PRB) and relative root mean squared error (RRMSE) of 4 classic cohort‐based methods, 3 size‐based methods, and several variants thereof were computed as a function of sampling frequency, distribution of sampling dates, number of quadrats sampled per occasion, inclusion or omission of the last sampling date, and coarseness of the size classes and sieve aperture. Although most methods performed reasonably well, non‐negligible differences were observed among them. A version of Allen's curve technique and a mass‐specific growth rate method gave the best results for cohort‐ and size‐based method groups, respectively. Sampling effort, in terms of both frequency of sampling and number of samples per date, had the largest documented influence on both PRB and RRMSE. Recommendations are made for the best compromises between methods and sampling designs to achieve reliable production estimates for populations with similar characteristics.