A comparison of methods to estimate abundance and biomass from belt transect surveys

It is becoming increasingly popular to use continuously collected acoustic or optical data to estimate abundance or biomass of fish and invertebrates. However, data from such systems are typically highly spatially autocorrelated and zero-inflated, and thus simple design-based estimation techniques are not applicable. Model-based estimation methods can be used to extrapolate observations along the observed track to larger areas. We tested the precision and accuracy of three model-based methods using both simulations and field data: Ordinary kriging (OK), Generalized Additive Models with kriged model residuals (GAM + OK), and Generalized Additive Mixed Models with kriged model residuals (GAMM + OK), along with a design-based method (stratified mean, SM). The GAMM + OK method treats small-scale variations as random effects, whereas the other approaches aggregate nearby data to reduce autocorrelation and random errors. We found that the GAM + OK method with relatively small aggregation lengths generally gave the best performance of the model-based methods in terms of both accuracy and precision, followed by GAMM + OK. SM estimates were more accurate and precise than the model-based estimates in the simulations, but only when the study region was stratified accurately. Based on the simulation and field data analysis results, we selected the GAM + OK method to estimate scallop abundance and biomass for the Georges Bank and the Mid-Atlantic Bight regions for the years 2011-2015. We also provided SM estimates based on careful stratifications to validate the model-based estimates.