About this Research Topic
Fisheries management is a complex socio-political process that needs to be supported by accurate, consistent scientific data on stock status and trends. This is even more relevant with the current rapidly changing condition of the oceans of the world, and necessitates spatio-temporal biological data at high resolution to address uncertainty in stock assessment and to adjust reference points.
This Research Topic aims to bring together advances and innovative approaches, e.g., on collection of biological data and estimation of biological parameters, to facilitate more integrative fisheries assessments. This integrative approach would allow to generate both, a more realistic evaluation of stock status and more ecosystem assessment strategies. Its applied relevance in the field of fishery science is straightforward because it prompts multiple advances from data collection to stock assessment. Technological and analytical advances as well as artificial intelligence (AI) have the potential to optimize highly time-consuming biological tasks that provide input data for fishery stock assessments. These advances are also key to dealing with the continuously increasing information on the biologically relevant structure of fish populations and connectivity among management units, e.g., triggering stock boundary revisions that need to be supported by accurate and updated data.
Original contributions from any fishery science discipline relevant to stock assessment are welcome, especially interdisciplinary approaches. The following topics and related ones are clear candidates to fit this Research Topic, but others will also apply:
Innovative advances in fisheries data collection, e.g., electronic monitoring and use of artificial intelligence for the automation of tasks such as fish age reading, species identification, and, age estimation using epigenetic clocks.
Advances in the estimation of biological parameters using new sampling strategies and analytical methods.
Development of frameworks to use historical fisheries datasets (in disaggregated or reaggregated format) in the assessment of species with revised stock boundaries.
Candidate algorithmic tools to achieve a realistic portrait of the genetic diversity status of exploited fisheries.
The genetic-demographic interface, i.e. fishing pressure versus genetic erosion Minimum genetic erosion thresholds to prevent the demographic rebound of fisheries. The role of migratory dynamics and genetic enrichment at mitigating for genetic erosion induced by overfishing.
The extent to which demographic metrics can be extrapolated, combined, or integrated with genetic metrics using available or future algorithms.
Advancement in AI tools to systematize a real-time assessment, that is, avoiding actual generation gaps between the assessment and the current real state of a stock.
Tentative technical roadmaps from calibration and validation of new data sources to the implementation of AI algorithms in the assessment.
Introducing useful data sources and ecosystem information into assessment aiming at optimizing scientific advice and fisheries management.
Incorporation of socio-economic-ecological aspects for a more integrative fisheries assessment.
This Research Topic aims to bring together advances and innovative approaches, e.g., on collection of biological data and estimation of biological parameters, to facilitate more integrative fisheries assessments. This integrative approach would allow to generate both, a more realistic evaluation of stock status and more ecosystem assessment strategies. Its applied relevance in the field of fishery science is straightforward because it prompts multiple advances from data collection to stock assessment. Technological and analytical advances as well as artificial intelligence (AI) have the potential to optimize highly time-consuming biological tasks that provide input data for fishery stock assessments. These advances are also key to dealing with the continuously increasing information on the biologically relevant structure of fish populations and connectivity among management units, e.g., triggering stock boundary revisions that need to be supported by accurate and updated data.
Original contributions from any fishery science discipline relevant to stock assessment are welcome, especially interdisciplinary approaches. The following topics and related ones are clear candidates to fit this Research Topic, but others will also apply:
Innovative advances in fisheries data collection, e.g., electronic monitoring and use of artificial intelligence for the automation of tasks such as fish age reading, species identification, and, age estimation using epigenetic clocks.
Advances in the estimation of biological parameters using new sampling strategies and analytical methods.
Development of frameworks to use historical fisheries datasets (in disaggregated or reaggregated format) in the assessment of species with revised stock boundaries.
Candidate algorithmic tools to achieve a realistic portrait of the genetic diversity status of exploited fisheries.
The genetic-demographic interface, i.e. fishing pressure versus genetic erosion Minimum genetic erosion thresholds to prevent the demographic rebound of fisheries. The role of migratory dynamics and genetic enrichment at mitigating for genetic erosion induced by overfishing.
The extent to which demographic metrics can be extrapolated, combined, or integrated with genetic metrics using available or future algorithms.
Advancement in AI tools to systematize a real-time assessment, that is, avoiding actual generation gaps between the assessment and the current real state of a stock.
Tentative technical roadmaps from calibration and validation of new data sources to the implementation of AI algorithms in the assessment.
Introducing useful data sources and ecosystem information into assessment aiming at optimizing scientific advice and fisheries management.
Incorporation of socio-economic-ecological aspects for a more integrative fisheries assessment.
Keywords: Historical Fishery Datasets; Integrative Fish Stock Assessment; New Algorithms and Artificial Intelligence; New Embeddable Data Sources; Population Structure and Connectivity; Genetic-Demographic Interface
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
