Fishing from Space: A Bayesian Model of Global Inland Fish Production

The importance of freshwater fisheries to global food security and human welfare is becoming increasingly clear.  Yet, the available estimates of global inland fish production are highly uncertain and the importance of inland fisheries is undervalued.  We provide a new method for estimating global inland fisheries production in lakes based on chlorophyll concentration.  We compiled a dataset of fishery production (N=286 lakes) and derived chlorophyll concentration for these lakes using the European Space Agency’s MEdium Resolution Imaging Spectrometer (MERIS) satellite images.  We developed a Bayesian model of fisheries production as a function of chlorophyll accounting for the functional relationships between chlorophyll, fish biomass, effort, and harvest.  The predictive ability of our model was validated using an independent fishery dataset, and we demonstrate the use of this model for predicting fishery production at large scales.  Our results show that remote-sensed chlorophyll data may be used to estimate fisheries production when direct in-situ measurements or fishery assessments are not available.  Our modeling approach provides a reappraisal of the current estimates of global freshwater fish production that is founded on an ecological framework.