Fishers’ knowledge improves the accuracy of food web model predictions
Bentley, J.W.; Serpetti, N.; Fox, C.; Heymans, J.J.; Reid, D.G. (2019). Fishers’ knowledge improves the accuracy of food web model predictions. ICES J. Mar. Sci./J. Cons. int. Explor. Mer 76(4): 897-912. https://dx.doi.org/10.1093/icesjms/fsz003
Fisher's knowledge offers a valuable source of information to run parallel to observed data and fill gaps in our scientific knowledge. In this study we demonstrate how fishers' knowledge of historical fishing effort was incorporated into an Ecopath with Ecosim (EwE) model of the Irish Sea to fill the significant gap in scientific knowledge prior to 2003. The Irish Sea model was fitted and results compared using fishing effort time-series based on: (i) scientific knowledge, (ii) fishers' knowledge, (iii) adjusted fishers' knowledge, and (iv) a combination of (i) and (iii), termed “hybrid knowledge.” The hybrid model produced the best overall statistical fit, capturing the biomass trends of commercially important stocks. Importantly, the hybrid model also replicated the increase in landings of groups such as “crabs & lobsters” and “epifauna” which were poorly simulated in scenario (i). Incorporating environmental drivers and adjusting vulnerabilities in the foraging arena further improved model fit, therefore the model shows that both fishing and the environment have historically influenced trends in finfish and shellfish stocks in the Irish Sea. The co-production of knowledge approach used here improved the accuracy of model simulations and may prove fundamental for developing ecosystem-based management advice in a global context.
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