Integrating survey and observer data improves the predictions of New Zealand spatio-temporal models

Abstract

AbstractIn many situations, species distribution models need to make use of multiple data sources to address their objectives. We developed a spatio-temporal modelling framework that integrates research survey data and data collected by observers onboard fishing vessels while accounting for physical barriers (islands, convoluted coastlines). We demonstrated our framework for two bycatch species in New Zealand deepwater fisheries: spiny dogfish (Squalus acanthias) and javelinfish (Lepidorhynchus denticulatus). Results indicated that employing observer-only data or integrated data is necessary to map fish biomass at the scale of the New Zealand exclusive economic zone, and to interpolate local biomass indices (e.g., for the east coast of the South Island) in years with no survey but available observer data. Results also showed that, if enough survey data are available, fisheries analysts should: (1) develop both an integrated model and a model relying on survey-only data; and (2) for a given geographic area, ultimately choose the index produced with integrated data or the index produced with survey-only data based on the reliability of the interannual variability of the index. We also conducted a simulation experiment, which indicated that the predictions of our spatio-temporal models are virtually insensitive to the consideration of physical barriers.