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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]

Aims: The objective of the study was to compare grade point averages (GPAs) on compulsory school exit exams (exam GPA) and educational attainment at age 16 and 20 for individuals with and without type 1 diabetes. Methods: This study was a population‐based retrospective cohort study, which included the 1991 to 1998 birth cohorts in Denmark. Follow‐up was conducted at age 16 and 20 (follow‐up period; 1 January, 2007 to 31 December, 2018). There were 2083 individuals with and 555,929 individuals without type 1 diabetes. Linear regression and generalized linear models compared outcomes with and without adjustments for socio‐economic characteristics. Results: A total of 558,012 individuals (51% males) were followed to the age of 20. Having type 1 diabetes was associated with a lower exam GPA when adjusting for socio‐economic status (difference: −0.05 (95% CI, −0.09 to −0.01), a higher relative risk of not completing compulsory school by age 16 (1.37, 95% CI, 1.22 to 1.53)), and a higher relative risk of not completing or being enrolled in upper secondary education by age 20 (1.05, 95% CI, 1.00 to 1.10). Haemoglobin A1c (HbA1c) <58 mmol/mol (7.5%), >7 BGM/day and insulin pump use were associated with better educational achievement. Conclusion: Type 1 diabetes was associated with a marginally lower exam GPA and a higher risk of not completing compulsory school by age 16 and lower educational attainment by age 20. The findings were modified by HbA1c, BGM and insulin pump use. [ABSTRACT FROM AUTHOR]

Local and regional trade networks in small-scale fisheries are important for food security and livelihoods across the world. Such networks consist of both economic flows and social relationships, which connect different production regions to different types of fish demand. The structure of such trade networks, and the actions that take place within them (e.g., people fishing, buying, selling), can influence the capacity of small-scale fisheries to provide sufficient fish in a changing social and ecological context. In this study, we aim to understand the importance of networks between different types of traders that access spatially-distinct fish stocks for the availability and variability of fish provision. We deployed a mixed-methods approach, combining agent-based modelling, network analysis and qualitative data from a small-scale fishery in Baja California Sur, Mexico. The empirical data allowed us to investigate the trade processes that occur within trade networks; and the generation of distinct, empirically-informed network structures. Formalized in an agent-based model, these network structures enable analysis of how different trade networks affect the dynamics of fish provision and the exploitation level of fish stocks. Model results reveal how trade strategies based on social relationships and species diversification can lead to spillover effects between fish species and fishing regions. We found that the proportion of different trader types and their spatial connectivity have the potential to increase fish provision. However, they can also increase overexploitation depending on the specific connectivity patterns and trader types. Moreover, increasing connectivity generally leads to positive outcomes for some individual traders, but this does not necessarily imply better outcomes at the system level. Overall, our model provides an empirically-grounded, stylized representation of a fisheries trading system, and reveals important trade-offs that should be considered when evaluating the potential effect of future changes in regional trade networks. [ABSTRACT FROM AUTHOR]

Background: Managing the chronic illness type 1 diabetes (T1D) is extremely demanding, especially during adolescence. Self‐efficacy is belief in one’s own capabilities and this is crucial for diabetes management. Having a valid method for measuring self‐efficacy is important. Objective: Our aims were to psychometrically validate a Danish version of the self‐efficacy in diabetes management (SEDM) questionnaire, and to examine the relationship between background variables and self‐efficacy. Methods: All Danish adolescents with T1D (n = 1075) were invited to participate in our study. In total, 689 agreed to participate and 602 completed the study. Data were collected using a web‐based survey. All participants were asked to provide a blood sample for HbA1c measurement. Graphical log‐linear Rasch modeling (GLLRM) was used to validate the questionnaire and its reliability was assessed using Monte Carlo simulation. Results: We found the questionnaire to be valid and reliable, but it had a dual structure that suggested a need for 2 separate subscales. One subscale related to practical (SEDM1) and the other to emotional (SEDM2) aspects of diabetes management. Both subscales were targeted toward adolescents with lower self‐efficacy and were associated with HbA1c. SEDM1 was influenced by treatment modality and age. In SEDM2 we found an interaction between age and sex. Conclusion: The Danish version of the SEDM questionnaire should be divided into two parts, each with a valid and reliable subscale for self‐efficacy measurement. The relationship between self‐efficacy and age seems to differ between boys and girls. [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]

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies [ABSTRACT FROM AUTHOR]

Harvesting has received most theoretical, empirical, and policy attention towards understanding common-pool resource dilemmas. Yet, pre-harvesting and post-harvesting activities influence harvesting outcomes as well. Broadening the analytical focus beyond harvesting is needed to imagine new ways of theorizing and governing the commons. Fishing—which is synonymous with harvesting—is a case in point. We contribute to a beyond-harvesting research agenda by incorporating concepts from common-pool resources theory that have not received enough attention in the literature. We compare two ubiquitous self-organizing strategies (i.e., fishing cooperatives and patron-client relationships) fishers use to access means of production and analyze their effects on the distribution of benefits resulting from harvesting. We use rarely available longitudinal data of monetary loans to fishers in Mexican small-scale fisheries and find that cooperatives can deliver broader distribution of benefits than patron-client relationships. Our study highlights the importance of historically and contextually situating analyses linking the effects of pre-harvesting processes on harvesting outcomes, and the benefits of broadening the scope of inquiry beyond a narrow policy attention on harvesting to move towards a fuller understanding of commons dilemmas. [ABSTRACT FROM AUTHOR]

Objective: Type 1 diabetes is associated with an increased risk of psychiatric morbidities. We investigated predictors and diabetes outcomes in a pediatric population with and without psychiatric comorbidities.Research Design and Methods: Data from the Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids) and National Patient Register were collected (1996-2015) for this population-based study. We used Kaplan-Meier plots to investigate whether age at type 1 diabetes onset and average glycated hemoglobin (HbA1c) levels during the first 2 years after onset of type 1 diabetes (excluding HbA1c at debut) were associated with the risk of being diagnosed with a psychiatric disorder. Mixed-effects linear and logistic regression models were used to analyze HbA1c, BMI, severe hypoglycemia (SH), or ketoacidosis as outcomes, with psychiatric comorbidities as explanatory factor.Results: Among 4,725 children and adolescents with type 1 diabetes identified in both registers, 1,035 were diagnosed with at least one psychiatric disorder. High average HbA1c levels during the first 2 years predicted higher risk of psychiatric diagnoses. Patients with psychiatric comorbidity had higher HbA1c levels (0.22% [95% CI 0.15; 0.29]; 2.40 mmol/mol [1.62; 3.18]; P < 0.001) and an increased risk of hospitalization with diabetic ketoacidosis (1.80 [1.18; 2.76]; P = 0.006). We found no associations with BMI or SH.Conclusions: High average HbA1c levels during the first 2 years after onset of type 1 diabetes might indicate later psychiatric comorbidities. Psychiatric comorbidity in children and adolescents with type 1 diabetes increases the risk of poor metabolic outcomes. Early focus on the disease burden might improve outcomes. [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]

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation. [ABSTRACT FROM AUTHOR]

Social-ecological systems (SES) are complex adaptive systems. Social-ecological system phenomena, such as regime shifts, transformations, or traps, emerge from interactions among and between human and nonhuman entities within and across scales. Analyses of SES phenomena thus require approaches that can account for (1) the intertwinedness of social and ecological processes and (2) the ways they jointly give rise to emergent social-ecological patterns, structures, and dynamics that feedback on the entities and processes that generated them. We have developed a framework of linked action situations (AS) as a tool to capture those interactions that are hypothesized to have jointly and dynamically generated a social-ecological phenomenon of interest. The framework extends the concept of an action situation to provide a conceptualization of SES that focusses on social-ecological interactions and their links across levels. The aim of our SE-AS (social-ecological action situations) framework is to support a process of developing hypotheses about configurations of ASs that may explain an emergent social-ecological phenomenon. We suggest six social-ecological ASs along with social and ecological action situations that can commonly be found in natural resource or ecosystem management contexts. We test the ability of the framework to structure an analysis of processes of emergence by applying it to different case studies of regime shifts, traps, and sustainable resource use. The framework goes beyond existing frameworks and approaches, such as the SES framework or causal loop diagrams, by establishing a way of analyzing SES that focuses on the interplay of social-ecological interactions with the emergent outcomes they produce. We conclude by discussing the added value of the framework and discussing the different purposes it can serve: from supporting the development of theories of the emergence of social-ecological phenomena, enhancing transparency of SES understandings to serving as a boundary object for interdisciplinary knowledge integration. [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]

Objective: Alterations in blood lipid concentrations in anorexia nervosa (AN) have been reported; however, the extent, mechanism, and normalization with weight restoration remain unknown. We conducted a systematic review and a meta‐analysis to evaluate changes in lipid concentrations in acutely‐ill AN patients compared with healthy controls (HC) and to examine the effect of partial weight restoration. Method: A systematic literature review and meta‐analysis (PROSPERO: CRD42017078014) were conducted for original peer‐reviewed articles. Results: Forty‐eight studies were eligible for review; 33 for meta‐analyses calculating mean differences (MD). Total cholesterol (MD = 22.7 mg/dL, 95% CI = 12.5, 33.0), high‐density lipoprotein (HDL; MD = 3.4 mg/dL, CI = 0.3, 7.0), low‐density lipoprotein (LDL; MD = 12.2 mg/dL, CI = 4.4, 20.1), triglycerides (TG; MD = 8.1 mg/dL, CI = 1.7, 14.5), and apolipoprotein B (Apo B; MD = 11.8 mg/dL, CI = 2.3, 21.2) were significantly higher in acutely‐ill AN than HC. Partially weight‐restored AN patients had higher total cholesterol (MD = 14.8 mg/dL, CI = 2.1, 27.5) and LDL (MD = 16.1 mg/dL, CI = 2.3, 30.0). Pre‐ versus post‐weight restoration differences in lipid concentrations did not differ significantly. Discussion: We report aggregate evidence for elevated lipid concentrations in acutely‐ill AN patients compared with HC, some of which persist after partial weight restoration. This could signal an underlying adaptation or dysregulation not fully reversed by weight restoration. Although concentrations differed between AN and HC, most lipid concentrations remained within the reference range and meta‐analyses were limited by the number of available studies. [ABSTRACT FROM AUTHOR]

Rising inequalities and accelerating global environmental change pose two of the most pressing challenges of the twenty-first century. To explore how these phenomena are linked, we apply a social-ecological systems perspective and review the literature to identify six different types of interactions (or "pathways") between inequality and the biosphere. We find that most of the research so far has only considered one-directional effects of inequality on the biosphere, or vice versa. However, given the potential for complex dynamics between socioeconomic and environmental factors within social-ecological systems, we highlight examples from the literature that illustrate the importance of cross-scale interactions and feedback loops between inequality and the biosphere. This review draws on diverse disciplines to advance a systemic understanding of the linkages between inequality and the biosphere, specifically recognizing cross-scale feedbacks and the multidimensional nature of inequality. [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]