Bias in spatiotemporal index standardization models caused by unbalanced sampling designs and allocation schemes

Abstract Relative indices of abundance are key to providing science advice for the management of many fish stocks. Historical focus has been on design-based estimation, wherein mathematical formulae for aggregating data into a single index are directly informed by the underlying sampling design. Recent efforts have moved toward using index standardization models instead. However, the impacts sampling designs and allocation schemes have on these index standardization models have not been fully examined. Using a spatio-temporal multinomial index standardization model developed for survey data obtained with longline fishing gear, we develop a simulation framework to analyze the effects of five different combinations of sampling designs and allocation schemes under various conditions. These simulations expose bias in model-based indices caused by non-proportional-to-area designs, which are common in surveys that have historically utilized design-based estimation. We explore the impact of robust statistical alternatives on this bias. This effort highlights an important source of bias and the importance of periodically reassessing allocation schemes and sampling designs for scientific surveys.