To better understand natural variation in sex across the animal kingdom and how sex influences physiology and behavior throughout the lifespan, it is critical that a diversity of organisms with different sexual phenotypes are represented in neuroendocrinology research, both in field and laboratory settings (reviewed in
McLaughlin et al., 2023;
Smiley et al., 2022). Moreover, because sex is dependent on the integration of multiple phenotypes and, thus, can have different effects on physiology and behavior (e.g.,
Munley et al., 2022c;
Solomon-Lane et al., 2016;
White et al., 2023), sex diversity and variability should be regularly incorporated into experimental approaches to enable researchers to disentangle whether and how sex influences each of these individual processes. Although “sex differences” have been investigated for decades (reviewed in
Ball and Ketterson, 2008;
Bangasser and Valentino, 2014;
McCarthy et al., 2012;
McCarthy and Nugent, 2015;
Trainor, 2011;
Yan and Silver, 2016), there is not a consensus on how to study sex in
animal models, both with respect to experimental design and statistical analysis. To date, sex diversity and variability in neuroendocrinology has primarily been studied in sex-stable species (i.e., species in which the gonads and related traits, physiological systems, and behavior reaches a
steady state after development;
Table 1; reviewed in
McLaughlin et al., 2023;
Smiley et al., 2022). Relatively little is known, however, about how these mechanisms may differ in sex-dynamic organisms (i.e., species in which the gonads and related traits, physiological systems, and behavior exhibit variability based on their environment throughout the lifespan;
Table 1). There is also considerable variation in which variables are measured in experiments, how these variables are quantified, and whether and how sex is incorporated into
statistical modeling and testing, making it challenging to explicate the role of sex in modulating neuroendocrine mechanisms and behavior. In this section, we provide recommendations for best practices when designing experiments that examine or integrate sex variability and diversity, including: 1) factors and variables to consider measuring, 2) implementing diverse model organisms, 3) mechanistic approaches for studying multi-leveled traits, and 4) integrating sex-associated variables into data and statistical analyses. Collectively, these guidelines will not only allow researchers to answer central questions about how the brain, behavior, and other attributes are influenced by sex, but will also enhance our understanding of how these processes may differ across species with diverse sexual systems.
3.1. Factors and variables to consider when studying sex diversity and variability
In order to take a truly integrative approach to studying sex-associated traits, we must be aware that the variables we are using to determine sex (e.g., morphological, hormonal, genetic) not only interact with and influence each other (
Fig. 1), but also occur and fluctuate in the context of other factors that we may or may not be able to measure. As integrative biologists who examine multiple traits and factors that relate to sex, we use this framework with the understanding that they are not hierarchical separations of importance (reviewed in
MacDougall-Shackleton, 2011). It is important to note that this section is not intended to be an exhaustive list of factors to consider when designing experiments, but as a launching point for discussion and reflection of past and present experimental design which aims to study sex (either as an independent or dependent variable). We also note that in many cases, it will be impossible to control for every factor/variable in an experiment, so part of our aim is to increase awareness of factors/variables that are important in study design and may affect sex-associated traits that are being measured.
