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Aquatic and terrestrial organisms respond to climate change differently by biological sex. A key challenge is to unravel the relative contribution of sex and climate change in the response mechanisms of individuals and the related cascading effects to populations and communities. This new understanding is essential to improve climate adaptation and mitigation strategies.

The ocean is facing unprecedented challenges with the escalating impacts of climate change and other pressures threating ecosystems and the many benefits they provide. Effective strategies for reversing the loss of biodiversity and ecosystem services rely on knowledge of how marine organisms, populations, and communities respond to environmental change. A fundamental, but often overlooked biological characteristic of organisms is biological sex – which is distinct from sociocultural gender. Here, we address: a) how sex influences the physiology, behavior and ecology of marine organisms, populations, and communities; b) how this knowledge is generated in current marine biology research; and c) how sex analysis may be more broadly applied in future research. Through review of sex analysis applications in marine biological research we find that sex broadly affects the morphology, physiology, behavior, and distribution of organisms and population across taxa, with evidence of sex-specific differences in survival to thermal stress, timing of biological mechanisms, and energetics. Sex analysis reveals pervasive effects of sex on a suite of biological processes and scales of biological organizations. We found that sex analysis is most commonly applied to studies of organisms’ life history traits and population dynamics, but possible effects on communities are rarely addressed. Existing evidence of sex-based differences across taxa and biological levels of organization highlights that including sex as a biological variable in research is essential to understanding marine systems and their responses to environmental change. To facilitate future integration of sex into marine biological research, we synthesize current approaches, propose solutions to methodological and logistical challenges, and lay out guidelines for future research.

This study explores associations between changing researcher demographics and research questions asked in the field of history. Specifically, we analyze developments in the discipline of history as women entered the field. We focus on gender in diachronic analysis of history dissertations from 1980 (when online data is first available) to 2015 and a select set of general history journals from 1950 to 2015. We use correlated topic modeling and network visualizations to map developments in research agendas over time and to examine how women and men have contributed to these developments. This quantitative, longitudinal study of history suggests a symbiotic relationship between the numbers of women entering the field and the emergence of new research questions in the field.

Additional file 1: Fig. S1. Flowchart of article inclusion and exclusion in the literature search. Fig. S2. Screeplot of the factor analysis reported in Table S8. Table S1. Item phrasing and descriptive statistics for the 44 potentially relevant gender-related items. Table S2. Response options for all 44 items included in the exploratory factor analyses. Table S3. Rank of gender characteristics based on occurrences (>2). Table S4. Search-terms for meta-analyses of existing scales measuring each gender variable. Table S5. Health-related items and response options. Table S6. Demographic items and response options. Table S7. Recoding of ten variables to allow for the largest possible sample in the EFA. Table S8. Exploratory Factor Analysis (Full factor model). Table S9. Communalities and unique variances for exploratory factor analysis presented in Table S8. Table S10. Exploratory Factor Analysis (Full factor model), Oblimin rotation. Table S11. Exploratory Factor Analysis (Full factor model), Varimax rotation. Table S12. Exploratory Factor Analysis (Full factor m odel), Equamax rotation. Table S13. Exploratory Factor Analysis (Full factor model), Quartimax rotation. Table S14. Factor loadings for CFA Models 1 and 2 in sample 1. Table S15. Factor loadings for CFA Samples 2 and 3 (Configural invariance). Table S16. Factor loadings for CFA Samples 2 and 3 (Metric invariance, 24 items). Table S17. Factor loadings for final CFA in samples 2 and 3 (Scalar invariance, 24 items). Table S18. Factor loadings for final CFA in samples 2 and 3 (Metric invariance, 25 items). Table S19. Factor loadings for final CFA samples 2 and 3 (Scalar invariance, 25 items). Table S20. Correlations between the factors in samples 1, 2 and 3. Table S21. Negative binomial regression predicting number of days with poor physical health (during past 30 days) (with gender identity as covariate). Table S22. Negative Binomial regression predicting number of days with poor mental health (during past 30 days) (with gender identity as covariate). Table S23. Negative binomial regression predicting number of days where poor mental or physical health prevented the respondent from doing usual activities (during past 30 days) (with gender identity as covariate). Table S24. Logistic regression predicting general health status (excellent, very good, good= 0, fair, poor= 1) (with gender identity as covariate). Table S25. Logistic regression predicting vaping (not vaping=0, vaping=1) (with gender identity as covariate). Table S26. Logistic regression predicting smoking (not smoking=0, smoking=1) (with gender identity as covariate). Table S27. Logistic regression predicting binge drinking (less than monthly=0, monthly, weekly, and daily=1) (with gender identity as covariate). Table S28. Logistic regression predicting overweight (BMI

Gender and sex analysis is increasingly recognized as a key factor in creating better medical research and health care 1–7 . Using a sample of more than 1.5 million medical research papers, our study examined the potential link between women’s participation in medical science and attention to gender-related and sex-related factors in disease-specific research. Adjusting for variations across countries, disease topics and medical research areas, we compared the participation of women authors in studies that do and do not involve gender and sex analysis. Overall, our results show a robust positive correlation between women’s authorship and the likelihood of a study including gender and sex analysis. These findings corroborate discussions of how women’s participation in medical science links to research outcomes, and show the mutual benefits of promoting both the scientific advancement of women and the integration of gender and sex analysis into medical research. Nielsen and colleagues’ analysis of a large database of medical research papers shows a correlation between women’s authorship and the likelihood of a study including gender and sex analysis.