Research Weaving: Visualizing the Future of Research Synthesis

We propose a new framework for research synthesis of both evidence and influence, named research weaving. It summarizes and visualizes information content, history, and networks among a collection of documents on any given topic. Research weaving achieves this feat by combining the power of two methods: systematic mapping and bibliometrics. Systematic mapping provides a snapshot of the current state of knowledge, identifying areas needing more research attention and those ready for full synthesis. Bibliometrics enables researchers to see how pieces of evidence are connected, revealing the structure and development of a field. We explain how researchers can use some or all of these tools to gain a deeper, more nuanced understanding of the scientific literature

orchaRd 2.0 : an R package for visualising meta-analyses with orchard plots

1. Although meta-analysis has become an essential tool in ecology and evolution, reporting of meta-analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. 2. Here, we have added significant enhancements by integrating many new functionalities into orchaRd 2.0. This updated version allows the visualisation of heteroscedasticity (different variances across levels of a categorical moderator), marginal estimates (e.g. marginalising out effects other than the one visualised), conditional estimates (i.e. estimates of different groups conditioned upon specific values of a continuous variable) and visualisations of all types of interactions between two categorical/continuous moderators. 3. orchaRd 2.0 has additional functions which calculate key statistics from multilevel meta-analytic models such as $I^{2}$ and $R^{2}$. Importantly, orchaRd 2.0 contributes to better reporting by complying with PRISMA-EcoEvo (preferred reporting items for systematic reviews and meta-analyses in ecology and evolution). Taken together, orchaRd 2.0 can improve the presentation of meta-analytic results and facilitate the exploration of previously neglected patterns. 4. In addition, as a part of a literature survey, we found that graphical packages are rarely cited (~3%). We plea that researchers credit developers and maintainers of graphical packages, for example, by citations in a figure legend, acknowledging the use of relevant packages

Sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioural traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the ‘estrus-mediated variability hypothesis’); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the ‘greater male variability hypothesis’. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications, including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance

Sex differences in allometry for phenotypic traits in mice indicate that females are not scaled males

Sex differences in the lifetime risk and expression of disease are well-known. Preclinical research targeted at improving treatment, increasing health span, and reducing the financial burden of health care, has mostly been conducted on male animals and cells. The extent to which sex differences in phenotypic traits are explained by sex differences in body weight remains unclear. We quantify sex differences in the allometric relationship between trait value and body weight for 363 phenotypic traits in male and female mice, recorded in >2 million measurements from the International Mouse Phenotyping Consortium. We find sex differences in allometric parameters (slope, intercept, residual SD) are common (73% traits). Body weight differences do not explain all sex differences in trait values but scaling by weight may be useful for some traits. Our results show sex differences in phenotypic traits are trait-specific, promoting case-specific approaches to drug dosage scaled by body weight in mice

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of ‘missingness’

Intergenerational effects of overfeeding on aversive learning in zebrafish (Danio rerio)

The obesity epidemic is concerning as obesity appears to negatively impact cognition and behavior. Furthermore, some studies suggest that this negative effect could be carried across generations from both mothers and fathers although evidence is not consistent. Here, we attempt to address how obesogenic diets in the parental generation (F0) can impact offspring's cognition and anxiety intergenerationally (F1) in a zebrafish model. We compare both mean trait values and their variances. Using a multifactorial design, we created a total of four groups: F1T (treatment mothers × treatment fathers); F1M (treatment mothers × control fathers); F1P (treatment fathers × control mothers); and F1C (control mothers × control fathers, F1C); and subjected them to anxiety tank tests and aversive learning assays. When both parents were exposed, offspring (F1T) displayed the poorest aversive learning, while offspring that only had one parent exposed (F1P and F1M) learnt the aversive learning task the best. Zebrafish in all groups displayed no statistically significant differences in anxiety-associated behaviors. Males and females also performed similarly in both anxiety and aversive learning assays. While all F1 groups had similar levels of fasting blood glucose, variance in glucose levels were reduced in F1P and F1T indicating the importance of investigating heteroskedasticity between groups. Furthermore, anxiety behaviors of these two groups appeared to be less repeatable. To our knowledge, this is the first study to test the intergenerational effects of an obesogenic diet on zebrafish cognition. Our multifactorial design as well as repeated tests also allowed us to disentangle maternal and paternal effects (as well as combined effects) and accurately detect subtle information such as between-individual variation

The effects of an obesogenic diet on behavior and cognition in zebrafish (Danio rerio): Trait average, variability, repeatability, and behavioral syndromes.

The obesity epidemic, largely driven by the accessibility of ultra-processed high-energy foods, is one of the most pressing public health challenges of the 21st century. Consequently, there is increasing concern about the impacts of diet-induced obesity on behavior and cognition. While research on this matter continues, to date, no study has explicitly investigated the effect of obesogenic diet on variance and covariance (correlation) in behavioral traits. Here, we examined how an obesogenic versus control diet impacts means and (co-)variances of traits associated with body condition, behavior, and cognition in a laboratory population of ~160 adult zebrafish (Danio rerio). Overall, an obesogenic diet increased variation in several zebrafish traits. Zebrafish on an obesogenic diet were significantly heavier and displayed higher body weight variability; fasting blood glucose levels were similar between control and treatment zebrafish. During behavioral assays, zebrafish on the obesogenic diet displayed more exploratory behavior and were less reactive to video stimuli with conspecifics during a personality test, but these significant differences were sex-specific. Zebrafish on an obesogenic diet also displayed repeatable responses in aversive learning tests whereas control zebrafish did not, suggesting an obesogenic diet resulted in more consistent, yet impaired, behavioral responses. Where behavioral syndromes existed (inter-class correlations between personality traits), they did not differ between obesogenic and control zebrafish groups. By integrating a multifaceted, holistic approach that incorporates components of (co-)variances, future studies will greatly benefit by quantifying neglected dimensions of obesogenic diets on behavioral changes

The effects of an obesogenic diet on behaviour and cognition in zebrafish (Danio rerio): traits average, variability, repeatability, and behavioural syndromes

The obesity epidemic, largely driven by the accessibility of ultra-processed high-energy foods, is one of the most pressing public health challenges of the 21st century. Consequently, there is increasing concern about the impacts of diet-induced obesity on behaviour and cognition. While research on this matter continues, to date, no study has explicitly investigated the effect of obesogenic diet on variance and covariance (correlation) in behavioural traits. Here, we examined how an obesogenic versus control diet impacts means and (co-)variances of traits associated with body condition, behaviour, and cognition in a laboratory population of ~160 adult zebrafish (Danio rerio). Overall, an obesogenic diet increased variation in several zebrafish traits. Zebrafish on an obesogenic diet were significantly heavier and displayed higher body weight variability; fasting blood glucose levels were similar between control and treatment zebrafish. During behavioural assays, zebrafish on the obesogenic diet displayed more exploratory behaviour and were less reactive to video stimuli with conspecifics during a personality test, but these significant differences were sex-specific. Zebrafish on an obesogenic diet also displayed repeatable responses in aversive learning tests whereas control zebrafish did not, suggesting an obesogenic diet resulted in more consistent, yet impaired, behavioural responses. Where behavioural syndromes existed (inter-class correlations between personality traits), they did not differ between obesogenic and control zebrafish groups. By integrating a multifaceted, holistic approach that incorporates components of (co-)variances, future studies will greatly benefit by quantifying neglected dimensions of obesogenic diets on behavioural changes

Sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the 'estrus-mediated variability hypothesis'); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the 'greater male variability hypothesis'. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance