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In the scientific literature on fisheries, the concept of community is often used broadly to indicate a place-based group whose members are dedicated to fisheries and have relatively homogeneous economic, social, and cultural interests. However, this categorical perspective to scope a "fishing community" is not necessarily an insightful approach to explore diverse social relationships with the marine environment, fishwork, and management in a practical context, and risks mismatches with science-based recommendations for management and policy. Drawing from ethnographic work, we highlight different historical and cultural dynamics from four case studies from fisheries in northwest Mexico. We identify key factors that help contextualize fishwork relationships, related to the importance of fishing practices on worldviews, daily routines, and income. These are used to derive three configurations (livelihood, way of life, and job) that describe and give analytical content to the notion of these fishing communities. Our use of a typology is not intended to generalize them or provide universal categories, but rather to convey to a broad range of fisheries scientists the importance of considering social contexts in the places in which we work and learn, and a set of guiding questions that may help in this regard. Contextualizing the importance of historical and cultural factors in scoping community units beyond occupational or geographical characteristics is essential for identifying and addressing (in)equitable processes and outcomes in fisheries sectors, research, and management. [ABSTRACT FROM AUTHOR]

The future of the global ocean economy is currently envisioned as advancing towards a ‘blue economy’—socially equitable, environmentally sustainable and economically viable ocean industries1,2. However, tensions exist within sustainable development approaches, arising from differing perspectives framed around natural capital or social equity. Here we show that there are stark differences in outlook on the capacity for establishing a blue economy, and on its potential outcomes, when social conditions and governance capacity—not just resource availability—are considered, and we highlight limits to establishing multiple overlapping industries. This is reflected by an analysis using a fuzzy logic model to integrate indicators from multiple disciplines and to evaluate their current capacity to contribute to establishing equitable, sustainable and viable ocean sectors consistent with a blue economy approach. We find that the key differences in the capacity of regions to achieve a blue economy are not due to available natural resources, but include factors such as national stability, corruption and infrastructure, which can be improved through targeted investments and cross-scale cooperation. Knowledge gaps can be addressed by integrating historical natural and social science information on the drivers and outcomes of resource use and management, thus identifying equitable pathways to establishing or transforming ocean sectors1,3,4. Our results suggest that policymakers must engage researchers and stakeholders to promote evidence-based, collaborative planning that ensures that sectors are chosen carefully, that local benefits are prioritized, and that the blue economy delivers on its social, environmental and economic goals.The capacity to create an equitable and sustainable ‘blue economy’ from ocean resources will be determined by addressing social conditions, governance and infrastructure, not just resource availability, as shown by a fuzzy logic model incorporating multidisciplinary criteria. [ABSTRACT FROM AUTHOR]

No-take marine reserves can be powerful management tools, but only if they are well designed and effectively managed. We review how ecological guidelines for improving marine reserve design can be adapted based on an area’s unique evolutionary, oceanic, and ecological characteristics in the Gulf of California, Mexico. We provide ecological guidelines to maximize benefits for fisheries management, biodiversity conservation and climate change adaptation. These guidelines include: representing 30% of each major habitat (and multiple examples of each) in marine reserves within each of three biogeographic subregions; protecting critical areas in the life cycle of focal species (spawning and nursery areas) and sites with unique biodiversity; and establishing reserves in areas where local threats can be managed effectively. Given that strong, asymmetric oceanic currents reverse direction twice a year, to maximize connectivity on an ecological time scale, reserves should be spaced less than 50-200 km apart depending on the planktonic larval duration of target species; and reserves should be located upstream of fishing sites, taking the reproductive timing of focal species in consideration. Reserves should be established for the long term, preferably permanently, since full recovery of all fisheries species is likely to take > 25 years. Reserve size should be based on movement patterns of focal species, although marine reserves > 10 km long are likely to protect ~ 80% of fish species. Since climate change will affect species’ geographic range, larval duration, growth, reproduction, abundance, and distribution of key recruitment habitats, these guidelines may require further modifications to maintain ecosystem function in the future. [ABSTRACT FROM AUTHOR]

Evaluating progress towards environmental sustainability goals can be difficult due to a lack of measurable benchmarks and insufficient or uncertain data. Marine settings are particularly challenging, as stakeholders and objectives tend to be less well defined and ecosystem components have high natural variability and are difficult to observe directly. Fuzzy logic expert systems are useful analytical frameworks to evaluate such systems, and we develop such a model here to formally evaluate progress towards sustainability targets based on diverse sets of indicators. Evaluation criteria include recent (since policy enactment) and historical (from earliest known state) change, type of indicators (state, benefit, pressure, response), time span and spatial scope, and the suitability of an indicator in reflecting progress toward a specific objective. A key aspect of the framework is that all assumptions are transparent and modifiable to fit different social and ecological contexts. We test the method by evaluating progress towards four Aichi Biodiversity Targets in Canadian oceans, including quantitative progress scores, information gaps, and the sensitivity of results to model and data assumptions. For Canadian marine systems, national protection plans and biodiversity awareness show good progress, but species and ecosystem states overall do not show strong improvement. Well-defined goals are vital for successful policy implementation, as ambiguity allows for conflicting potential indicators, which in natural systems increases uncertainty in progress evaluations. Importantly, our framework can be easily adapted to assess progress towards policy goals with different themes, globally or in specific regions. [ABSTRACT FROM AUTHOR]