Computational approaches improve evidence synthesis and inform broad fisheries trends

Addressing ecological impacts with effective conservation actions requires information on the links between human pressures and localized responses. Understanding links is a priority for many conservation contexts, including the world's fresh waters, which face intensifying threats to disproportionately high species diversity, including more than half of the world's fish species. Literature synthesis can uncover links and highlight potential research gaps, yet can be very cumbersome and time consuming. Emerging tools like text mining can improve efficiency in extracting relevant information from vast scientific outputs. This study synthesizes evidence of direct anthropogenic threats to major inland fisheries and examines driver-impact-response patterns using coupled automated and manual text classification methods. We screened 9336 abstracts from 45 river basins of high importance to inland fish production; 1152 abstracts contained evidence of direct threats to fish. The most common documented drivers were pollution, dams, and fishing pressure, which were most strongly linked to decreased fitness, altered reproduction, and mortality, respectively. Strong impact-response links to pollution signal potential bias toward documenting acute threats that generate more visible and immediate impacts. The use of machine learning-based text classification performed best in classifying extraneous information. Results can inform the development of inland fisheries indicators and threat-based metrics, highlight possible evidence gaps in linking global drivers to fishery-level responses, and illustrate the application of a coupled synthesis approach for improved efficiency and extraction of information relevant to conservation outcomes. The associated user and interpretation guides address accessibility and technical barriers faced by conservation scientists to improve efficiency in evidence synthesis.