Divergent Circadian Foraging Strategies in Response to Diurnal Predation Versus Persistent Rain in Asian Weaver Ant, Oecophylla smaragdina, Suggest Possible Energetic Trade-offs.

The study of chronobiology of foraging behavior in social insects offers valuable models for the investigation of circadian rhythms. We scored hourly nest entries and exits of Oecophylla smaragdina (Asian weaver ant) workers in 9 active non-polydomous nests on days with and without rain and with and without a primarily diurnal predator present. After determining that Oecophylla display a high nest fidelity, we focused exclusively on analyzing nest entry counts: we found a significant decrease in overall entry counts of individual ants on rainy days compared with non-rainy days (p < 0.0001). They usually maintain a typical diurnal pattern of foraging activity; however, that regularity was often distorted during rainy periods but appeared to quickly revert to typical patterns following rain. This lack of compensatory foraging activity following a period of rain supports the hypothesis that these ants have enough food reserves to withstand a pure masking-induced suppression of foraging activity. Predation through bird anting, too, decreased foraging activity but appeared to cause a reversal in foraging activity timing from diurnal to nocturnal foraging. Daily periodicity of foraging was significantly disrupted in most nests during rain; however, daily foraging periodicity was disrupted in only one nest due to presence of predators. Thus, rain and predation both exert significant impacts on the overall foraging activity of Asian weaver ants, but while persistent pressure from rain seemed to primarily cause masking (diminution) of circadian foraging activity, predation restricted to the daytime resulted in phase-inversion to nocturnal foraging activity, with little diminution. This is consistent with different energetic strategies being used in response to different pressures by this species.

Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study.

Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Variability of temperature measurements recorded by a wearable device by biological sex.

BACKGROUND: Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales. METHODS: Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments. RESULTS: Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales. CONCLUSIONS: These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

Women are still excluded from research disproportionately, due in part to documented concerns that menstrual cycles make them more variable and so harder to study. In the past, we have challenged this claim, finding it does not hold for animal physiology, animal behavior, or human behavior. Here we are able to show that it does not hold in human physiology either. We analyzed 6 months of continuously collected temperature data measured by a commercial wearable device, in order to determine if it is true that females are more variable or less predictable than males. We found that temperatures mostly vary as a function of time of day and whether the individual was awake or asleep. Additionally, for some females, nightly maximum temperature contained a cyclical pattern with a period of around 28 days, consistent with menstrual cycles. The variability was different between cycling females, not cycling females, and males, but only cycling female temperature contained a monthly structure, making their changes more predictable than those of non-cycling females and males. We found the majority of unexplained variance to be within each sex/cycling category, not between them. All groups had indistinguishable measurement errors across time. This analysis of temperature suggests data-driven characteristics might be more helpful distinguishing individuals than historical categories such as binary sex. The work also supports the inclusion of females as subjects within biological research, as this inclusion does not weaken statistical comparisons, but does allow more equitable coverage of research results in the world.