Managing fisheries to meet social, economic and ecological objectives is a fundamental problem encountered in fisheries management worldwide. In Australia, fisheries management involves a complex set of national and sub-national policy arrangements, including those designed to deliver against ecologically sustainable development (ESD) objectives. The complex policy framework makes ensuring policy coherence and avoiding unintended consequences difficult, particularly where potential trade-offs are not made explicit. Coherence, or potential policy weakness, of Australian fisheries management in relation to ESD objectives was examined in a subset of Australian wild capture fisheries, at national and jurisdictional scales. Coherent policy frameworks with ESD objectives were found to be more likely at the legislative-level across jurisdictions (horizontal coherence), than other levels of implementation. Many fisheries had problems demonstrating coherence between legislation and management plans due to lack of inclusion of ESD policy themes at management and operational levels. Case studies revealed substantial variation in the likelihood for horizontal and vertical coherence between fisheries policy frameworks managing the same species. The lack of explicit ESD objectives observed in many Australian fisheries suggests a high likelihood of incoherence in fisheries management, or alternatively that managers may be informally persuing higher levels of policy coordination and coherence than can be detected. Lack of detectability of coherence is problematic for demonstrating accountability and transparency in decision-making and public policy. Furthermore, use of discretion by managers when developing management plans, in order to overcome policy weakness, may lead to drifts in individual management direction within a jurisdiction.

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/BMSY). We also compare several other models to estimate B/BMSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/BMSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.

The ubiquitous spread of digital networks has created techniques which can organize, store, and analyse large data volumes in an automizedand self-administered manner in real time. These technologies will have profound impacts on policy, administration, economy, trade, society,and science. This article sketches how three digital data technologies, namely the blockchain, data mining, and artificial intelligence could impact commercial fisheries including producers, wholesalers, retailers, consumers, management authorities, and scientist. Each of these three technologies is currently experiencing an enormous boost in technological development and real-world implementation and is predicted to increasingly affect many aspects of fisheries and seafood trade. As any economic sector acting on global scales, fishing and seafood production are often challenged with a lack of trust along various steps of the production process and supply chain. Consumers are often not well informed on the origin and production methods of their product, management authorities can only partly control fishing and trading activities and producers can be challenged by low market prices and competition with peers. The emerging data technologies can improve the trust among agents within the fisheries sector by increasing transparency and availability of information from net to plate.

Implementing a governance transformation entails the creation of a new institutional system when ecological, economic, or social structures make the existing system untenable. It involves building capacities, establishing viable formal and informal institutions, and triggering major societal changes. Early assessments (EAs) provide a mechanism to fine-tune and support institutional learning processes, which are needed to provide legitimacy and political acceptability of transformational change. We performed an EA of a governance transformation aimed at implementing ecosystem-based, multilevel participatory fisheries management in Chile. We performed individual interviews and workshops and synthesized existing reports to assess the main challenges of the institutionalization of the new policy. Results showed that successful implementation of the governance transformation would need to address key issues related to building trust and improving transparency, including clear protocols for cocreating knowledge and securing resources and capacities. The EA allowed us to define specific recommendations associated with legal reforms, issuing of new executive orders to clarify implementation, and improvement in operational standards by government agencies. EAs provide a fundamental tool that helps build legitimacy and sustainability of new governance systems. They bring a sense of reality, informed by social science, that allows us to understand progress in the implementation of governance transformations, by identifying rigidities that fail to accommodate emerging realities.