Abstract

Simple SummaryIt is not known if an energetically demanding process, such as egg laying, can affect corticosterone measured in turkey feathers and blood plasma, or if there are differences between feather types and genetic lines. The objective of this study was to compare hormone levels from feathers and plasma taken before and during egg laying. We found that the corticosterone in the feathers were higher during egg laying, but only for one genetic line. This genetic line produces more eggs and, so, the elevated corticosterone may reflect the higher metabolic investment in producing eggs. Increases in corticosterone levels were found in different feather types; however, the overall hormone level differed between feather types. Unlike the feathers, we found a decrease in plasma hormone measures between the two periods which might reflect the influence of gonadal development or suppression of acute hormone production during lay. From this study, we conclude that feathers can be used to detect increases in corticosterone during periods of high energy demand, but the genetic line needs to be considered, and the results may not correspond with plasma measures. This study also reinforces the requirement for consistent feather sampling when using corticosterone levels in feathers as non-invasive biomarkers.Phenological differences in energy demand (i.e., reproductive status) might influence the measurement of corticosterone. The objective of this study was to compare corticosterone concentrations in feathers (FCORT) and plasma (PCORT) for turkey hens before and during egg laying. Secondary feathers 1 and 3, and a plasma sample were collected from 50 hens at 30 weeks (before egg laying) from two purebred lines. The hens were reexamined during lay (45 weeks) to collect regrown feathers and plasma samples. Corticosterone concentrations were measured using an ELISA. Linear mixed models were used to assess the effect of genetic line (A vs. B) and period (pre-lay vs. lay) on FCORT and PCORT levels. An increase in FCORT during lay was detected for line B (p < 0.0001), but not line A (p = 0.3076). An increase in FCORT during lay was detectable in both feather types, but there was a difference between secondary 1 and 3 in FCORT concentration within each line studied. Conversely, PCORT decreased between the pre-lay and lay periods for both lines, although the decrease was more substantial for line A (p < 0.0001). Differences in metabolic investment in egg production between the two genetic lines may explain the different FCORT response during lay. The results from this study provide insight into how periods of high energy demand may influence corticosterone which should be considered when interpreting results.

Highlights

  • Corticosterone is the predominant avian glucocorticoid hormone which is an endproduct of the hypothalamic-pituitary-adrenal (HPA) axis [1]

  • The effect of egg laying on FCORT was significantly influenced by genetic line, with increases in FCORT during egg laying only being observed in the line selected for reproductive traits

  • The results of this study demonstrate that understanding individual variation in life-history investment may help explain the large amount of variability seen in studies of FCORT and plasma corticosterone (PCORT)

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Summary

Introduction

Corticosterone is the predominant avian glucocorticoid hormone which is an endproduct of the hypothalamic-pituitary-adrenal (HPA) axis [1]. Measured differences in corticosterone may reflect HPA-axis activity in response to perturbations, it is often not considered, or known, how underlying factors might influence these measurements before we can make inferences about welfare [3]. Studies of physical or environmental perturbations often have a variable effect on corticosterone measurements which could possibly be attributed to differences in phenology, underlying metabolic requirements which might influence glucocorticoid secretion [3,4,5]. To truly determine whether corticosterone provides an insight into animal welfare or stress, we need to determine how underlying metabolic demands might influence these processes

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