Management thinker Peter Drucker is often quoted as saying that “you can't manage what you can't measure.” Drucker means that you cannot know whether or not you are successful unless success is defined and monitored. Such a quote is fully applicable to fishery science because it is only when we can estimate the status of stocks that we can provide meaningful and successful management advice: that which gets measured, gets managed. However, an increasing share of fishers' income, is derived from fish from stocks whose status remains unassessed. In such situations, a simple rough model might be more useful than no model at all.
The main reasons for the lack of assessment and associated formal harvest control rules are often associated to:
• lack of (quality) data to reliably inform a fully integrated stock assessment;
• limited capacity and funding;
• associated fishery characteristics, including inconsistent targeting practices, numerous unregulated operators, or profound cultural issues;
• the challenge of selecting, from numerous possibilities, the most appropriate assessment and management options given the fishery’s context.
However, many methods have been developed to assist in the assessment of the status of so-called data-limited stocks. Although not based on complex integrated models increasingly used in stock assessments, data-limited assessment methods, particularly when paired with precautionary harvest control rules, provide a reliable understanding of stock status and might be used to achieve fishery sustainability.
The primary goal of the proposed Topic is to accommodate studies conducted around the world and pertaining to data-limited methods in fishery stock assessment and management. We invite manuscripts that consider:
• Assessment and forecasting approaches for data-limited species.
• Particular circumstances in which data-limited stocks are hampering or undermining a formal management process.
• Formal evaluations comparing data-limited and data-rich assessment methods.
• Toward quantification of robustness of data-poor methods: Uncertainty and sensitivity
• Improvements in, and novel approaches to, data-limited stock assessment methods.
• Formal management (harvest) strategies for data-limited fisheries
Overall, this Research Topic intends to provide a ground for discussing the potential for data-poor methods to be applied in fishery assessments as well as limitations on their use. Moreover, the studies should cover a management perspective with a clear objective of resource conservation, sustainable exploitation, economic viability and a combination of these and other aims. Although many of the data-poor studies concentrate on the assessment of the status of biological resources, research to understand the economic drivers of fisheries, as well as the management systems in place, are highly recommended. As management needs to be based on scientific advice, we encourage the submission of other article type formats that can be beneficial for simple management guidelines with regards to data-poor fisheries, e.g. Policy Briefs, Policy and Practice Reviews.
Management thinker Peter Drucker is often quoted as saying that “you can't manage what you can't measure.” Drucker means that you cannot know whether or not you are successful unless success is defined and monitored. Such a quote is fully applicable to fishery science because it is only when we can estimate the status of stocks that we can provide meaningful and successful management advice: that which gets measured, gets managed. However, an increasing share of fishers' income, is derived from fish from stocks whose status remains unassessed. In such situations, a simple rough model might be more useful than no model at all.
The main reasons for the lack of assessment and associated formal harvest control rules are often associated to:
• lack of (quality) data to reliably inform a fully integrated stock assessment;
• limited capacity and funding;
• associated fishery characteristics, including inconsistent targeting practices, numerous unregulated operators, or profound cultural issues;
• the challenge of selecting, from numerous possibilities, the most appropriate assessment and management options given the fishery’s context.
However, many methods have been developed to assist in the assessment of the status of so-called data-limited stocks. Although not based on complex integrated models increasingly used in stock assessments, data-limited assessment methods, particularly when paired with precautionary harvest control rules, provide a reliable understanding of stock status and might be used to achieve fishery sustainability.
The primary goal of the proposed Topic is to accommodate studies conducted around the world and pertaining to data-limited methods in fishery stock assessment and management. We invite manuscripts that consider:
• Assessment and forecasting approaches for data-limited species.
• Particular circumstances in which data-limited stocks are hampering or undermining a formal management process.
• Formal evaluations comparing data-limited and data-rich assessment methods.
• Toward quantification of robustness of data-poor methods: Uncertainty and sensitivity
• Improvements in, and novel approaches to, data-limited stock assessment methods.
• Formal management (harvest) strategies for data-limited fisheries
Overall, this Research Topic intends to provide a ground for discussing the potential for data-poor methods to be applied in fishery assessments as well as limitations on their use. Moreover, the studies should cover a management perspective with a clear objective of resource conservation, sustainable exploitation, economic viability and a combination of these and other aims. Although many of the data-poor studies concentrate on the assessment of the status of biological resources, research to understand the economic drivers of fisheries, as well as the management systems in place, are highly recommended. As management needs to be based on scientific advice, we encourage the submission of other article type formats that can be beneficial for simple management guidelines with regards to data-poor fisheries, e.g. Policy Briefs, Policy and Practice Reviews.
