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A social-ecological network model (panels a and b) allowed to map the diversification strategies of small-scale fisheries actors consisting on switching their target species within one region or between landing regions (i.e., spatial diversification, requiring the spatial displacement or mobility of fisheries actors). We analyzed changes in diversification strategies from 2008 to 2016 in Baja California Sur, Mexico, which included a moderate La Niña event characterized by colder sea surface temperatures (2011) and a strong El Niño characterized by warmer sea surface temperatures (2015). We found that even if species diversification within one region is more prevalent than spatial diversification, inter-anual changes in spatial diversification are, correlated with oceanographic changes. However, the diversification patterns differ between landing regions (e.g., panels c and d) and between species targeted. We investigate these differences in light of the characteristics of species groups and existing literature, and provide hypotheses regarding the environmental and institutional factors that may influence the observed diversification patterns. Overall, our novel social-ecological network model proved useful to understand multi-species diversification patterns across time and space. We shed light into the dynamics and structures of small-scale fisheries' diversification patterns that are increasingly important in the current era of global change. • We use social-ecological networks to analyze diversification strategies over time. • We analyze spatial and species diversification in tandem. • Changes in spatial diversification are correlated with oceanographic changes. • Institutional factors may constrain or enable spatial diversification. • Management needs to account for spatial diversification in a changing climate. Small-scale fisheries' actors increasingly face new challenges, including climate driven shifts in marine resource distribution and productivity. Diversification of target species and fishing locations is a key mechanism to adapt to such changes and maintain fisheries livelihoods. Here we explore environmental and institutional factors mediating how patterns of spatial diversification (i.e., utilization of alternative fishing grounds) and target species diversification change over time. Using small-scale fisheries in Baja California Sur (Mexico) as a case study, we adopt a social-ecological network approach to conduct a spatially explicit analysis of fisheries landings data (2008–2016). This approach quantifies relative patterns of diversification, and when combined with a qualitative analysis of existing literature, enables us to illuminate institutional and environmental factors that may influence diversification strategies. Our results indicate that interannual changes in spatial diversification are correlated with regional oceanographic change, while illustrating the heterogeneity and dynamism of diversification strategies. Rather than acting in isolation, we hypothesize that environmental drivers likely operate in combination with existing fisheries regulations and local socioeconomic context to mediate spatial diversification. We argue that small-scale fisheries policies need to better account such linkages as we move towards an increasingly variable environment. Overall, our results highlight spatial diversification as a dynamic process and constitute an important step towards understanding and managing the complex mechanisms through which environmental changes affect small-scale fisheries. [ABSTRACT FROM AUTHOR]

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]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different specific questions, but also vary widely in system conceptualization and complexity. With each model, certain ecological factors and mechanisms were analyzed in detail, while others were included but remained unchanged, or were excluded. Model-based causal analyses of the same system are thus inherently constrained by diverse implicit assumptions about possible determinants of causation. In developing recommendations for human action, awareness is needed of this strong context dependence of causal claims, which is often not entirely clear. Model comparisons can be supplemented by integrating findings from multiple models and confronting models with multiple observed patterns. • We reviewed how ecological models are used to study causes of complex phenomena. • Models for the same system were developed and analyzed in quite different ways. • Model-based causal claims strongly depend on context, which is often not entirely clear. • Awareness of hidden assumptions improves clarity and robustness of causal findings. [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]

Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents' behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence. • This article reviews agent-based modeling's generic features. • Understanding agents' behaviors is a top priority in agent-based modeling. • Artificial intelligence and data science can empower agents to self-learn their behavioral rules on-the-fly. • An RL-CNN approach is presented showing machine learning can be used to derive agent behavioral rules. [ABSTRACT FROM AUTHOR]

Humanity is facing many grand challenges at unprecedented rates, nearly everywhere, and at all levels. Yet virtually all these challenges can be traced back to the decision and behavior of autonomous agents that constitute the complex systems under such challenges. Agent-based modeling has been developed and employed to address such challenges for a few decades with great achievements and caveats. This article reviews the advances of ABM in social, ecological, and socio-ecological systems, compare ABM with other traditional, equation-based models, provide guidelines for ABM novice, modelers, and reviewers, and point out the challenges and impending tasks that need to be addressed for the ABM community. We further point out great opportunities arising from new forms of data, data science and artificial intelligence, showing that agent behavioral rules can be derived through data mining and machine learning. Towards the end, we call for a new science of Agent-based Complex Systems (ACS) that can pave an effective way to tackle the grand challenges. [ABSTRACT FROM AUTHOR]