Predicting yolk testosterone allocation using ecological contexts and species-specific traits.

Abstract

The prenatal transfer of testosterone (T) from mother to offspring is an important source of phenotypic plasticity. In birds, exposure to ecologically relevant stimuli, such as social competition or an attractive mate, can cause females to deposit more T into their egg yolks. Exposure to elevated yolk T can modify the expression of several fitness-related traits in offspring (e.g. growth, immune function, secondary sex traits and behaviour). Despite some of these changes being potentially adaptive, not all studies find that yolk T levels change in response to ecologically relevant stimuli. This heterogeneity is currently unexplained, limiting our ability to predict inter-generational responses to ecological change. Here, we performed a systematic literature search and found 119 observations across 39 wild species that measured inter-female variation in yolk T allocation in response to various stimuli. We used boosted regression trees, a form of machine learning, to examine whether species-specific traits or variation in study-level variables could explain variation in yolk T allocation (i.e. statistically significant vs. non-significant responses). We found that both species-specific traits and study-level variables are important predictors of significant changes in yolk T levels. Geographic range (latitude and longitude), evolutionary distinctiveness, longevity, egg mass relative to female mass, sociality, migration status and time to fledge were among the top 10 most influential predictors of the 48 examined. We also found that studies measuring or manipulating social stimuli (e.g. competition and breeding density) or breeding date were more likely to detect changes in yolk T allocation compared with studies examining other ecological contexts. Overall, these data provide several testable hypotheses concerning yolk T allocation and its adaptive value across species and contexts. Additionally, these findings can help us predict how ecological changes will affect hormonal responses in females that can shape future generations.