Marine ecosystem management is increasingly expected to take into account a wide range of ecological and socio‐economic factors. Decision‐making is helped by end‐to‐end ecosystem models that allow exploration of alternative management scenarios given a complex range of interacting factors. We present Atlantis – a spatially structured largely deterministic end‐to‐end marine ecosystem model written in C, available for all major operating systems, based on dynamically interacting physics, biology, fisheries, management, assessment and economics submodels. A detailed installation guide and example application files are also provided. One of the main features of Atlantis is its modularity. At the simplest level Atlantis can have uniform forcing of oceanographic processes, a single primary producer and a consumer. At the most complex level, Atlantis can be used with a range of environmentally driven ecological responses, complex and habitat‐dependent food web, dynamic assessment, management and fishing effort driven by market forces and human behaviour. The combination chosen should be guided by the available data and the questions to be answered. Atlantis provides a large and customizable list of output files and summary statistics that can be analysed and plotted using a number of dedicated r packages. When applying the Atlantis package, the users should be aware of the caveats associated with complex models, such as parameter and structural model uncertainty and challenges interpreting interactions of multiple processes.

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Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller's experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers.Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. Highlights► Best practices for calibrating complex process-based ecosystem models are described. ► We emphasize the importance of understanding the model structure and data sources. ► Strategies to estimate core parameters for an Atlantis ecosystem model are defined. ► Key diagnostic routines to help modelers identify erroneous parameters are outlined. ► A pedigree to evaluate ecosystem model confidence and data quality is proposed.

Juvenile jack mackerel were found in 2009 in the Challenger break and the East Pacific ridge (CHAEPR). This seamount region is ∼3500 km from the coastal historic jack mackerel nursery grounds off Chile (north of 30°S). We reviewed historic evidence of juveniles around this seamount and data on several local environmental conditions: sea surface temperature, chlorophyll-a, wind, turbulence levels, and Eddy kinetic energy (EKE). A Lagrangian model for the early life stages of jack mackerel in the eastern South Pacific was used to assess the potential of the seamount region as a permanent nursery ground. Transport/retention mechanisms were assessed by releasing virtual particles coupled to a growth model into the flow simulated by an eddy-resolving ocean model. Model simulations showed high inter-annual variability for particle retention in the seamount region; high retention levels were associated with low EKE such that the particles were retained for several months. Satellite altimetry has shown a local minimum in eddy activity in the region where the juveniles were observed; this minimum was consistent with the above temporal relationship. The inclusion of the CHAEPR oceanic seamount region as a potential nursery ground for jack mackerel expands the current conceptual framework for the spatial population structure of this species in the South Pacific off central Chile proposed by Arcos et al. (The jack mackerel fishery and El Niño 1997–98 effects off Chile. Progress in Oceanography 49: 597–617, 2001). Finally, we discuss the relevance of seamounts playing a double role (spawning and nursery grounds) from the standpoint of conservation and biodiversity.

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