  Many wild species affected by human activities require multiple surveys with
differing designs to capture behavioural response to wide ranging habitat
conditions and map and quantify them. While data from for example intersecting
but disparate fish surveys using different gear, are widely available,
differences in design and methodology often limit their integration. Novel
statistical approaches which can draw on observations from diverse sources
could enhance our understanding of multiple species distributions
simultaneously and thus provide vital evidence needed to conserve their
populations and biodiversity at large. Using a novel Bayesian hierarchical
binomial-lognormal hurdle modelling approach within the INLA-SPDE framework, we
combined and analysed acoustic and bottom trawl survey data for herring, sprat
and northeast Atlantic mackerel in the North Sea. These models were implemented
using INLA-SPDE techniques. By accounting for gear-specific efficiencies across
surveys in addition to increased spatial coverage, we gained larger statistical
power with greatly minimised uncertainties in estimation. Our statistical
approach provides a methodological development to improve the evidence base for
multispecies assessment and marine ecosystem-based management. And on a broader
scale, it could be readily applied where disparate biological surveys and
sampling methods intersect, e.g. to provide information on biodiversity
patterns using global datasets of species distributions.
