My new paper is available for free download for 50 days (until November 9, 2016). The paper is published in the journal Ecological Economics and discusses modeling of marine food webs such that economic analysis is viable. At the core of our approach lie the ensemble Kalman filter, something I have used earlier. In this new application, we go further in reducing model parameter dimensionality and move beyond the filtering routine to estimate certain structure parameters. We also apply a data transformation that deal with previously overlooked endogeneity in stock level data. We use all this to estimate a model of the largest pelagic fish stocks in the Norwegian Sea. The abstract:
While economists have discussed ecosystem-based fisheries management and similar concepts, little attention has been devoted to purposeful modeling of food webs. Models of ecosystems or food webs that make economic analysis viable should capture as much as possible of system structure and dynamics while balancing biological and ecological detail against dimensionality and model complexity. Relevant models need strong, empirical content, but data availability may inhibit modeling efforts. Models are bound to be nonlinear, and model and observational uncertainty should be included. To deal with these issues and to improve modeling of ecosystems or food webs for use in ecosystem-based fisheries management analysis, we suggest the data assimilation method ensemble Kalman filtering. To illustrate the method, we model the dynamics of the main, pelagic species in the Norwegian Sea. In order to reduce parameter dimensionality, the species are modeled to rely on a common carrying capacity. We also take further methodological steps to deal with a still high number of parameters. Our best model captures much of the observed dynamics in the fish stocks while the estimated model error is moderate.
The paper is part of the EINSAM project.