Chelsie Romulo | Search USGS

USGS2024

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.

Conservation Science and Practice↗

USGS2017

Assessing the global distribution of river fisheries harvest: A systematic map protocol

Background Although surface freshwater comprises < 0.01% of the total water volume of earth, freshwater inland capture fisheries and aquaculture represent 40% of the global reported finfish harvest. While the social, economic, and ecological importance of inland fish and fisheries is difficult to overstate, they are often undervalued and underappreciated. Accurate information about these highly dispersed fisheries is inherently difficult to acquire, often unreported, and not collected in a standardized format globally. A standardized river fishery database is needed for managing aquatic systems as well as for defining relevant development policies. Here, we describe our methodology to search, identify, and describe available river fisheries information to create a harmonized global database of river fisheries harvest. This database will provide the first global database of spatially and temporally explicit river fisheries data. The database can be used to identify locations, hotspots of data collection, and gaps in existing knowledge and will be especially important to inform studies and management at larger spatial scales (i.e., watershed, regional, or global scales). This database will also be critical for developing fish biomass models for rivers, which can provide managers with information critical for decision-making, such as improved valuation methods for river fish and fisheries. Methods This systematic map protocol describes the methodology to search, identify, and describe available information on river fish and fisheries across the globe. We define river fisheries as “both capture and aquaculture of river finfish species for food, income, or recreation”. River fish species are those finfish that live part, or all of their lives in rivers. The searches will be conducted for the period from 1950 to present using bibliographic databases and grey literature sources. To identify relevant evidence, pre-defined inclusion and exclusion criteria will be used to screen articles at title, abstract, and full text. A searchable database containing extracted meta-data from relevant included studies will be developed and presented as a geodatabase. The final systematic map will consist of a descriptive narrative report of the distribution and content of river fish literature including a geodatabase of available information.

Environmental Evidence↗