| Summary: | Modelling is important for impact assessments of anthropogenic pressures on wildlife. Models are particularly useful when dealing with complex dynamic systems (as pelagic ecosystems) where data are limited and if various ‘what if’ scenarios should be tested. The aim of this study was to produce and implement an integrated modelling approach, linking high resolution hydrodynamic models (HDM) of the marine environment with correlative species distribution models (SDM) and agent-based models (ABM), for describing the spatio-temporal distribution and movements of Atlantic mackerel (Scomber scombrus) in the Norwegian Sea. The SDM was fitted with scientific mackerel trawl data as response variables (collected in July and August 2006–2014) and temperature (from the HDM), water depth and time period as predictors of spatial distributions. The SDM was able to produce dynamic predictions of a similar order of magnitude as observed catch per unit effort (CPUE) as well as realistic large-scale distribution patterns, when tested on independent data (not included in the modelling). The ABM was calibrated, with normalized SDM predictions (habitat suitability as a proxy for food availability) and hydrodynamics as input and simulated on a single year (2013) for the period May–October, when the migratory mackerel is present in the study area. A pattern-oriented modelling (POM) approach was used to verify if the model reproduced multiple observed real-world patterns. The ABM produced similar patterns as observed regarding migration timing, growth and large scale geographic distribution. Fine scaled information on mackerel movement and behaviour is limited, which is also reflected in the results. More data and knowledge are therefore required to improve the patterns emerging from fine scaled processes. The potential of the model for assessing an impact of a single seismic survey (mimicking a real survey) was finally evaluated. The exercise allowed estimating the number of affected fish (within 50 km from the sound source) and ...
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