The Benefits of Hierarchical Ecosystem Models: Demonstration Using EcoState, a New State‐Space Mass‐Balance Model

Abstract

Ecosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate‐linked dynamics in ecosystem‐based fisheries management. However, fishery regulations are primarily informed by single‐species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” and (4) attributing process errors via comparison amongst different models. We then demonstrate these by introducing a new state‐space model EcoState (and associated R‐package) that extends mass balance dynamics from Ecopath with Ecosim. This model estimates mass balance (Ecopath) and time‐dynamics (Ecosim) parameters directly via their fit to time‐series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real‐world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self‐test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state‐space mass‐balance models can be fitted to time‐series data (similar to surplus‐production stock assessment models), and can attribute time‐varying productivity to both bottom‐up and top‐down drivers including the contribution of individual predator and prey interactions.