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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]

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