Fisheries management in the face of capacity, data, and climate challenges
Creators: name: Free, Christopher M.
Publisher: name: No Publisher Supplied
Year: 2018

description: The sustainable management of global fisheries is essential to addressing food and income security in the coming century. Although fisheries management has advanced significantly over the past few decades, a number of challenges still make the determination of sustainable catch limits difficult to impossible. Many fisheries remain unassessed due to a lack of capacity or lack of data to conduct stock assessments. Furthermore, even when catch limits can be determined, illegal, unreported, and unregulated fishing undermine their effectiveness. Finally, modern fisheries management is complicated by climate change, which is altering population dynamics through large-scale redistributions, changes in phenology, altered food availability, and habitat degradation. In my dissertation, I examine the manifestation of these three challenges – limited capacity, limited data, and climate change – in fisheries of small-, medium-, and large-scales, respectively.   In Chapter 1 (small-scale, limited capacity), I used a mixed-method approach to describe the extent, character, and motivations of illegal fishing in Lake Hovsgol National Park, Mongolia and its impact on the lake’s fish populations, especially that of the endangered endemic Hovsgol grayling (Thymallus nigrescens). I show that illegal fishing threatens the Hovsgol grayling but also provides food and income for park residents. An effective management system must therefore incorporate the needs of local people while also addressing the synergistic pressures of climate change, water pollution, increasing tourism, and invasive species.   In Chapter 2 (medium-scale, limited data), I evaluated the performance of the ORCS Working Group Approach to estimating stock status and overfishing limits for ‘catch-only’ fisheries. I show that the approach is a poor predictor of status and should not be used by managers. I subsequently refined the approach using a machine learning algorithm trained on data-rich stocks and show that the refined ORCS approach performs better than other widely used catch-only methods and can be used when data-moderate methods are not possible or appropriate.   In Chapter 3 (large-scale, climate change), I used surplus production models with monotonic temperature-dependence to measure the influence of sea surface temperature (SST) on the productivity of 190 global fish stocks. I show that ocean warming has significantly positively and negatively influenced the productivity of 20 and 14 stocks, respectively (34 total; 18% total). The influence of warming on a stock’s productivity is determined by ecoregion, taxonomic family, life history, and exploitation history. Hindcasts of SST-dependent maximum sustainable yield indicate that MSY of assessed stocks decreased 12.4% from 1930 to 2010. These results show that we must adjust expectations for future food production from the ocean even as the global human population and demand for seafood grows.  Together, these chapters work to help fisheries management overcome challenges from capacity shortfalls, data limitations, and climate change.