Baseline glucocorticoids alone do not predict reproductive success across years, but in interaction with enzymatic antioxidants

Abstract Glucocorticoids are known to adjust organismal functions, such as metabolism, in response to environmental conditions. Therefore, these hormones are thought to play a key role in regulating the metabolically demanding aspects of reproduction, especially in variable environments. However, support for the hypothesis that variation in glucocorticoid concentrations predicts reproductive success is decidedly mixed. Two explanations may account for this discrepancy: (i) Glucocorticoids might not act independently but could interact with other physiological traits, jointly influencing reproduction, and (ii) such an association could become apparent primarily in challenging environments when glucocorticoid concentrations increase. To address these two possibilities, we determined natural variation in circulating baseline glucocorticoid concentrations in parental great tits (Parus major) alongside two physiological systems known to be related with an individual's metabolism: oxidative status parameters (i.e., concentrations of pro‐oxidants, dietary, and enzymatic antioxidants) and body condition. These systems interact with glucocorticoids and can also influence reproductive success. We measured these variables in two breeding seasons that differed in environmental conditions. When accounting for the interaction of baseline glucocorticoids with other physiological traits, we found a positive relationship between baseline glucocorticoids and the number of fledglings in adult great tits. The strength of this relationship was more pronounced for those individuals who also had high concentrations of the enzymatic antioxidant glutathione peroxidase. When studied independently, glucocorticoids were not related to fitness proxies, even in the year with more challenging environmental conditions. Together, our study lend to support the hypothesis that glucocorticoids do not influence fitness alone, but in association with other physiological systems.


Supporting Information
Table 1: Results from a two linear mixed-effect models estimating variation in the number and mass of fledglings in relation during the two breeding seasons.We used the residuals of a linear regression between the number/mass of the nestlings and clutch size as the dependent variable.We fitted 'year ' (2015 vs 2016) as a fixed factor and 'nest ID' as a random factor.We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.while accounting for the influence of oxidative status markers and body condition.In each model, we fitted fledgling number and mass corrected by clutch size (i.e., using the residuals of a linear regression of fledgling number or mass and clutch size on day 15 as the dependent variable) as response variables.We scaled and included all five physiological variables as covariates and 'nest ID' as random factor.We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.
Oxidative status markers: OXY, non-enzymatic antioxidants in plasma; GPX, enzymatic antioxidant in red blood cells; ROMs, reactive oxygen metabolites in plasma.Table 3: Results from two linear mixed-effect models testing if glucocorticoids interact with oxidative status markers to jointly predict reproductive success.In each model, we fitted fledgling number and mass corrected by clutch size (i.e., using the residuals of a linear regression of fledgling number or mass and clutch size on day 15 as the dependent variable) as response variables.We included the interaction between baseline corticosterone and the three parameters of oxidative status as covariates and 'nest ID' as random factor.We scaled all covariates.We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.Oxidative status markers: OXY, non-enzymatic antioxidants in plasma; GPX, enzymatic antioxidant in red blood cells; ROMs, reactive oxygen metabolites in plasma.to jointly predict reproductive success.In each model, we fitted fledgling number and mass corrected by clutch size (i.e., using the residuals of a linear regression of fledgling number or mass and clutch size on day 15 as the dependent variable) as response variables.We included the interaction between baseline corticosterone and body condition as covariates and 'nest ID' as random factor.We scaled all covariates.
We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.reproductive fitness traits in a context-dependent way.In each model, we fitted fledgling number and mass corrected by clutch size (i.e., using the residuals of a linear regression of fledgling number or mass and clutch size on day 15 as the dependent variable) as response variables.We included baseline corticosterone as a covariate, 'year' (2015 vs 2016) as a fixed factor, along with their interaction, and 'nest ID' as random factor.We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.

Table 2 :
Results from two linear mixed-effect models testing if glucocorticoids predict reproductive success

Table 4 :
Results from two linear mixed-effect models testing if glucocorticoids interact with body condition

Table 5 :
Results from two linear mixed-effect models testing if that baseline corticosterone influences

Table 6 :
Results from linear models testing the differences in each of the five physiological parameters between the two years.In each model, we fitted 'year' (2015 vs 2016) as a fixed factor.We considered an effect size to be "statistically significant" when the estimated CrI did not overlap the zero.Oxidative status markers: OXY, non-enzymatic antioxidants in plasma; GPX, enzymatic antioxidant in red blood cells; ROMs, reactive oxygen metabolites in plasma.