The results from this research was recently published in Nature, one of the most prestigious scientific venues publishing interdisciplinary research. The article can be read here at Nature's website (subscription required).
If a doe (a female deer) can influence the sex of her offspring, which sex should she prefer to produce, in order to get a fitness advantage for her offspring and hence maximise the number of her grandchildren? A daughter is a sure bet as a daughter will likely produce a fawn each year, largely independent of her own condition, whereas bearing a son is potentially more rewarding but also is riskier: a good-condition son may become the harem leader and sire a large number of offspring, however a low-quality son will not be able to compete with the incumbent harem leader and hence have a low chance to sire offspring at all. So given her own condition and assuming that her own condition is inherited in part to her children, should a doe produce a son or daughter to maximise the number of grandchildren, i.e., her future offspring?
The classical Trivers-Willard Theory hypotheses that poor-condition females should produce daughters to ensure reproduction of a low, but certain number of offspring, and that good-condition females should produce sons who – being good condition themselves – have a realistic chance to become harem leaders. However empirical evidence for this behaviour is contradictory.
The authors of the Nature article demonstrated that the mono-causal dependence of which sex to produce based on only the mother’s quality is too simplistic. In fact, the relative advantage of producing one sex over the other depends on a number of factors, for example the higher risk for males to die at a given age than for females, which ecologists summarise with the term life history. The authors developed a multi-causal model of which sex to produce. With this they can explain the apparent contradictions in earlier theories and successfully predict which sex a female produces in a giving mating system. The process leading to this successful model involved advanced mathematical methods and computer simulations requiring strong interdisciplinary collaboration.
"I really enjoyed working as a member of our international team of outstanding researchers from the UK, South Africa, Canada, France and the US. It was exactly that type of truly interdisciplinary work that I so enjoy, with Ecologists, Biologists, Mathematicians and Computer Scientists involved." says Dr Grüning. "Only as an interdisciplinary team were we able to address this ecological research question which is important to inform for example conversation plans."