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Meeting the objectives of sustainable fisheries management requires attention to the complex interactions between humans, institutions and ecosystems that give rise to fishery outcomes. Traditional approaches to studying fisheries often do not fully capture, nor focus on these complex interactions between people and ecosystems. Despite advances in the scope and scale of interactions encompassed by more holistic methods, for example ecosystem‐based fisheries management approaches, no single method can adequately capture the complexity of human–nature interactions. Approaches that combine quantitative and qualitative analytical approaches are necessary to generate a deeper understanding of these interactions and illuminate pathways to address fisheries sustainability challenges. However, combining methods is inherently challenging and requires understanding multiple methods from different, often disciplinarily distinct origins, demanding reflexivity of the researchers involved. Social–ecological systems' research has a history of utilising combinations of methods across the social and ecological realms to account for spatial and temporal dynamics, uncertainty and feedbacks that are key components of fisheries. We describe several categories of analytical methods (statistical modelling, network analysis, dynamic modelling, qualitative analysis and controlled behavioural experiments) and highlight their applications in fisheries research, strengths and limitations, data needs and overall objectives. We then discuss important considerations of a methods portfolio development process, including reflexivity, epistemological and ontological concerns and illustrate these considerations via three case studies. We show that, by expanding their methods portfolios, researchers will be better equipped to study the complex interactions shaping fisheries and contribute to solutions for sustainable fisheries management. [ABSTRACT FROM AUTHOR]

Small-scale fisheries (SSFs) in developing countries are expected to play a significant role in poverty alleviation and enhancing food security in the decades to come. To realize this expectation, a better understanding of their informal self-governance arrangements is critical for developing policies that can improve fishers’ livelihoods and lead to sustainable ecosystem stewardship. The goal of this paper is to develop a more nuanced understanding of micro-level factors—such as fishers’ characteristics and behavior—to explain observed differences in self-governance arrangements in Northwest Mexico. We focus on two ubiquitous forms of self-governance: hierarchical non-cooperative arrangements between fishers and fishbuyers, such as patron-client relationships (PCs), versus more cooperative arrangements amongst fishers, such as fishing cooperatives (co-ops). We developed an agent-based model of an archetypical SSF that captures key hypotheses from in-depth fieldwork in Northwest Mexico of fishers’ day-to-day fishing and trading. Results from our model indicate that high diversity in fishers’ reliability, and low initial trust between co-op members, makes co-ops’ establishment difficult. PCs cope better with this kind of diversity because, in contrast to co-ops, they have more flexibility in choosing whom to work with. However, once co-ops establish, they cope better with seasonal variability in fish abundance and provide long-term security for the fishers. We argue that existing levels of trust and diversity among fishers matter for different self-governance arrangements to establish and persist, and should therefore be taken into account when developing better, targeted policies for improved SSFs governance. [ABSTRACT FROM AUTHOR]

As a consequence of global environmental change, management strategies that can deal with unexpected change in resource dynamics are becoming increasingly important. In this paper we undertake a novel approach to studying resource growth problems using a computational form of adaptive management to find optimal strategies for prevalent natural resource management dilemmas. We scrutinize adaptive management, or learningby- doing, to better understand how to simultaneously manage and learn about a system when its dynamics are unknown. We study important trade-offs in decision-making with respect to choosing optimal actions (harvest efforts) for sustainable management during change. This is operationalized through an artificially intelligent model where we analyze how different trends and fluctuations in growth rates of a renewable resource affect the performance of different management strategies. Our results show that the optimal strategy for managing resources with declining growth is capable of managing resources with fluctuating or increasing growth at a negligible cost, creating in a management strategy that is both efficient and robust towards future unknown changes. To obtain this strategy, adaptive management should strive for: high learning rates to new knowledge, high valuation of future outcomes and modest exploration around what is perceived as the optimal action. [ABSTRACT FROM AUTHOR]

Adaptive management incorporates learning-by-doing (LBD) in order to capture learning and knowledge generation processes, crucial for sustainable resource use in the presence of uncertainty and environmental change. By contrast, an optimization approach to management identifies the most efficient exploitation strategy by postulating an absolute understanding of the resource dynamics and its inherent uncertainties. Here, we study the potential and limitations of LBD in achieving optimal management by undertaking an analysis using a simple growth model as a benchmark for evaluating the performance of an agent equipped with a 'state-of-the-art' learning algorithm. The agent possesses no a priori knowledge about the resource dynamics, and learns management solely by resource interaction. We show that for a logistic growth function the agent can achieve 90% efficiency compared to the optimal control solution, whereas when a threshold (tipping point) is introduced, efficiency drops to 65%. Thus, our study supports the effectiveness of the LBD approach. However, when a threshold is introduced efficiency decreases as experimentation may cause resource collapse. Further, the study proposes that: an appropriate amount of experimentation, high valuation of future stocks (discounting) and, a modest rate of adapting to new knowledge, will likely enhance the effectiveness of LBD as a management strategy. [Copyright &y& Elsevier]

Small-scale fisheries (SSF) contribute substantially to global food security, sustainable marine ecosystems and poverty alleviation. Yet many SSF face problems of overexploitation and poverty calling for novel governance approaches that enhance human-wellbeing, equity and ecological sustainability. External policies and interventions to support such governance transformations, however, need to take their often self-governed nature into account. Common practices based on informal arrangements between different fishery actors can make existing, mal-adapted structures very persistent and hence difficult to overcome. Here we combine multi-method empirical research on SSF in Mexico with agent-based modeling to analyze if and under which conditions interventions can shift ongoing self-organizing dynamics into directions that support the new governance form. We are particularly interested in the effectiveness of two different types of interventions, financial and social, and their performance under variable social and ecological conditions as commonly found in SSF. Our analysis reveals that a combination of financial and social support during extended periods of time is necessary to ensure persistence of new governance forms in face of competition with established forms, as well as environmental and social uncertainty. The findings highlight the importance of understanding the endogenous self-organizing dynamics created by the interplay between social (e.g. the dynamics of trust) and ecological (e.g. resource dynamics) processes in order to devise policies and measures to initiate a shift towards more sustainable pathways. • Combination of financial and social support needed for transforming governance. • Interventions need to be applied over extended periods of time. • Additional actions are needed to build resilience in uncertain world. • Combining empirical research with agent-based modelling allowed studying dynamic complexity. • Reinforcing feedbacks are particularly critical for overcoming lock-ins and accelerating adoption. [ABSTRACT FROM AUTHOR]

When reasoning about causes of sustainability problems and possible solutions, sustainability scientists rely on disciplinary-based understanding of cause–effect relations. These disciplinary assumptions enable and constrain how causal knowledge is generated, yet they are rarely made explicit. In a multidisciplinary field like sustainability science, lack of understanding differences in causal reasoning impedes our ability to address complex sustainability problems. To support navigating the diversity of causal reasoning, we articulate when and how during a research process researchers engage in causal reasoning and discuss four common ideas about causation that direct it. This articulation provides guidance for researchers to make their own assumptions and choices transparent and to interpret other researchers’ approaches. Understanding how causal claims are made and justified enables sustainability researchers to evaluate the diversity of causal claims, to build collaborations across disciplines, and to assess whether proposed solutions are suitable for a given problem. [ABSTRACT FROM AUTHOR]

A key aim of sustainable development is the joint achievement of prosperity, equality, and environmental integrity: in other words, material living standards that are high, broadly-distributed, and low-impact. This has often been called the "triple bottom line". But instead, what if there is a "trilemma" that inhibits the simultaneous achievement of these three goals? We analysed international patterns and trends in the relationships between per-capita gross national income, the Gini coefficient for income distribution, and per-capita ecological footprint from 1995 to 2017, benchmarking them against thresholds from the existing literature. A "dynamic" analysis of the trajectories of 59 countries and a "static" analysis of a larger sample of 140 countries found that none met the triple bottom line, and that instead there were widespread tradeoffs among the three indicators. These tradeoffs, leading to divergent national trajectories and country clusters, show that common pair-wise explanations such as Kuznets Curves do not adequately capture important development dynamics. In particular, while only a few countries simultaneously met the thresholds for prosperity and equality on the one hand and equality and environment on the other, none did for prosperity and environment. Moreover, inequality likely makes resolving this critical tradeoff more difficult. Our findings suggest that mitigating the sustainability trilemma may require countries – especially those that are already prosperous – to prioritize economic redistribution and environmental stewardship over further growth. • No country have achieved high prosperity, low inequality, and low environmental. • There is a global sustainability "trilemma" as opposed to a "triple bottom line". • This global challenge is unevenly distributed, an inequality trap seems to exist. • There were widespread tradeoffs among the three indicators. • Mitigating the prosperity-environment tradeoff is made more difficult by high inequality. [ABSTRACT FROM AUTHOR]