This symposium brings together scientists from across the globe to explore integration of probability and non-probability survey methods in fisheries to generate robust, efficient, and cost-effective data for decision-making.
Probability-based survey designs are becoming increasingly expensive and challenging to implement, especially for offsite surveys where responses to mail and telephone surveys have declined. Meanwhile, non-probability-based or convenience approaches have developed rapidly in recent years, such as those driven by access to people through social media. Coupled with advances in analytical methods, non-probability-based approaches demonstrated potential for generating robust population level estimates, as seen in election exit polls.
In commercial and recreational fisheries surveys, integrating these two approaches has the potential to improve the spatiotemporal resolution, improve cost-effectiveness, and generate robust outcomes. However, the best strategies for integrating these methods in a fisheries context remain unclear.
Objectives
The overall objective of the symposium is to assess the potential to integrate probabilistic and non-probabilistic methods in fisheries surveys, synthesize examples from fisheries, and address approaches to overcome key challenges and limitations of the methods.
Recommendations will be developed to inform the future direction for integrating sampling methods, including research priorities. Specific objectives include:
- Assessing how probability and non-probability methods are being integrated.
- Identifying examples of integration within commercial and recreational fisheries.
- Investigating how these approaches can be used to balance cost, precision, and accuracy.
- Highlighting research priorities and making recommendations for implementation in fisheries.
The anticipated outcome will be a greater shared understanding of how fishing surveys can be undertaken to provide high quality information for management purposes. A special edition publication is planned.
