My very first contribution to the collected sum of human knowledge has just been published in the Journal of Evolutionary Biology. The paper is called: “A Dynamic Framework for the Study of Optimal Birth Intervals Reveals the Importance of Sibling Competition and Mortality Risks” and you can read it online or download a PDF. I’ll talk about the main results in this post.
Birth spacing is an interesting topic not only from an evolutionary point of view but also as a public health issue. Human children need a lot of looking after and having many young children can make this much harder. Very short birth intervals are linked to higher risks of the mother and children (especially newborns) dying, to low birth weight and premature birth. The World Health Organisation recommends waiting at least two years before, in their words, “attempting the next pregnancy”.
We created a mathematical model – simulating evolution in a computer, essentially – to look at how mortality rates as well as competition between children can affect a female’s decisions about when to give birth and the amount of time to leave between births (‘reproductive scheduling’ and ‘birth intervals’ in the jargon).
Evolution will tweak the timings of biological life events (such births intervals, or the length of childhood, or your first period) in ways that increase your chances of reproducing and surviving in order to raise your children so they in turn have a chance of reproducing. Perhaps in humans our lifespans have been selected so that we live long enough to help raise our grandchildren too. This idea of having children that themselves will have children is called ‘reproductive success’. Evolution will design organisms to act in ways that try to achieve the highest possible reproductive success in the face of all the adversity the world throws at them.
We’re interested in birth intervals because organisms ‘want’ to get their genes into the next generation, and the timing of reproduction affects this. One of our assumptions is that the females in our model will behave in a manner that is best for them and their children given the environment they live in and the dangers posed by the world.
(In evolutionary biology, we use intentional language such as genes ‘wanting’ to reproduce or your body ‘deciding’ the ‘best’ time to begin menstruating or stop reproducing. This is a handy shortcut for us that can cause a lot of confusion when encountered by people not used to evolutionary science. The ‘decisions’ we talk about are biological events shaped by the processes of evolution. Reproduction will not necessarily be a conscious decision and I’m not suggesting that all females ‘want’ to reproduce – indeed, many humans choose not to make babies at all. Why people might choose not to reproduce is a superb but separate question. For now, for the sake of our model, assume that humans, like other animals, will tend to reproduce.)
Our model attempts to find the best (that is to say: optimal) pattern of spacing births that achieves the highest possible reproductive success, given the constraints of the world. The things we’re interested in are the risk of dying depending on age and environment, as well as how children can affect their siblings’ survival.
Babies and infants have a higher risk of dying compared to kids over the age of five (that’s true, to a greater or lesser extent, in all human populations). A mother’s death can lead to children dying if they still depend on her for sustenance. Children might also die if a mother has a lot of other kids and can’t provide for all of them. This last point is the ‘sibling competition’ we refer to in the title of our paper.
A mother dying (or her children dying) will lower her reproductive success. Over time, the model works out the optimal birth patterns that will increase her reproductive success — depending on how old she is, how old her children are, and how many children she has.
To look at how mortality rates affected birth spacing, we took data from Aché hunter-gatherers and Tsimane horticulturalists* from South America, Mandinka farmers from the Gambia, Taiwanese famers, and the national population of Sweden circa the mid-1960s. This let us look at how different rates of infant deaths or adult deaths – and the risk of dying due to accidents – might affect decisions to give birth.
We found that birth spacing gets shorter in places with high rates of infant mortality and senescent mortality (that is, the increased risk of dying as you get older). In these cases, the best strategy is to reproduce faster in order to have children before you die.
Regardless of the population we used in the model, birth spacing pretty much followed the same pattern (see graph below) – the length of time between births fluctuated but increased until around the age of 30. After this, birth spacing didn’t really vary until the model female stopped reproducing at age 50.
We also let siblings compete with one another to varying degrees. When competition was particularly fierce, the average time between births jumped up by up to one-and-a-quarter years. Sibling competition had a negligible effect on birth spacing in harsh environments, such as that faced by the Tsimane foragers. In ‘easier’ places, such as Sweden, the time between births increased as the ferocity of competition increased – suggesting that sibling competition might be important in places with low mortality, as has been shown in real life.
The full article will be behind a paywall for one year – after that time, you can read it online for free. But we’ve made all the code and data freely available (released under the GNU General Public License, version 2):
- Data: http://datadryad.org/resource/doi:10.5061/dryad.q3r3k
- Source code: https://github.com/matthewgthomas/optimal-birth-intervals
- Analysis code: https://github.com/matthewgthomas/optimal-birth-intervals-stats
* The published version of our paper refers to Tsimane people as hunter-gatherers, although it is more correct to call them horticulturalists or forager-horticulturalists since they grow corn, manioc, plantain and rice, as well as fish, hunt and gather foods from the forest.