Model-based calibration of gear-specific fish abundance survey data as a change-of-support problem

Abstract

For commercial and recreational fisheries of a wide-ranging species to be sustainable, abundance studies from neighboring regions should be unified. For the first time in the USA, a single research project to estimate the abundance of the Greater Amberjack {Seriola dumerili) is being undertaken at the continental scale. A major methodological challenge lies in 1) the difference in fish detection gears deployed by regional survey teams that produce gear-specific relative abundance indices, and 2) the unknown relationship between actual abundance and these indices. In this paper, we develop a conversion tool that is operationalized from a Bayesian hierarchical model in an inferential context akin to the change-of-support problem often encountered in large-scale spatial studies; though, the context here is to reconcile abundance data observed at various gear-specific scales. To this end, we consider a small calibration experiment in which 2 to 4 different underwater video camera types were simultaneously deployed on each of 21 boat trips. Alongside the suite of deployed cameras was also an acoustic echosounder that recorded fish signals along surrounding transects. Our modeling framework is used to derive calibration formulae for translating camera-specific relative indices to the actual abundance scale in surveys that deploy a single camera. Cross-validation is conducted using mark-recapture abundance estimates (only available for 10 trips, all observed at a single habitat type) and through a separate simulation study. We also briefly discuss the case when surveys pair one camera with the echosounder.