Assessing the effects of sampling design and data integration in spatio-temporal fisheries models

Abstract

Many communities around the world depend on the socio-cultural and economic benefits derived from fisheries. To ensure these continued benefits, fisheries management relies on accurate estimates of fish population size and health obtained through the stock assessment process. This thesis develops and improves upon various statistical methods used within the stock assessment process. We first show how common sampling designs do not strongly impact model results, but also how sampling effort allocation can introduce time-varying biases if the strata-specific sampling effort is not proportional to strata size. We next turn our attention to analyses that occur early in the stock assessment process, such as length-weight relationships. While complex statistical models are often necessary to estimate these relationships, their outputs are routinely used without accounting for their associated uncertainties. We present simple methods to highlight the increase in precision achieved by error propagation. The next two chapters focus on developing methods for the inclusion of novel types of data within stock assessment models. The first is a benthoscape map. Focusing on sea scallops in the Bay of Fundy, we demonstrate how benthoscape maps can be interpreted as habitat features and help inform the estimation of catchability and probabilities of encounter to improve parameter estimation and identify biologically meaningful area boundaries. The second is drop camera data. We propose various model modifications to account for the resolution difference between datasets. This work identifies a previously unknown relationship between the size of survey tows, the underlying spatial distribution of the population, and the accuracy of the survey. High aggregation or high noise in the spatial distribution results in the survey indices being more precise than the drop camera indices even with substantially smaller sample sizes. This thesis develops novel statistical methods useful to the broader field of fisheries science, with contributions for improved data integration, error propagation, and sampling design, while opening various new avenues of future work in all these same fields.