Managing natural resources can be difficult, but it’s essential to making sure that ecosystems aren’t too disrupted. This is doubly so when there are indigenous people living in the region in question, such as the Makushi or Wai Wai people of southern Guyana in South America. The Amazon is home to a number of indigenous groups, many of who have lived there for hundreds of years with minimal contact with the outside world.
Helping those people retain their traditional lifestyles is an important goal, but it often comes into conflict with procuring natural resources, or with expanding agricultural or urban spaces needed to sustain other people. In these cases, it can be very difficult to predict how indigenous peoples or the ecosystems in which they live will adapt to such changes.
A Stanford University team of researchers has developed a computer model that allows for some level of prediction in these situations, and is now working to implement the model in Brazilian policy. The model makes use of several “drivers,” or factors that cause changes in the environment. They include the introduction of advanced health care, the abandonment of traditional beliefs, conversion of land outside the indigenous territory, and introducing outside food sources.
They found, for example, that while converting land for agriculture use had a steep impact initially, it leveled out over time, and a new balance as struck. There were less biodiversity and fewer plants and animals overall within the indigenous region, but over time these things worked themselves out to a new balance.
On the other hand though, they found that introducing new food types led to an increase in population, which in turn put more stress on the ecosystem as more animals were hunted and food was harvested.
The time span of the simulation was about 250 years, but the model can help us to understand the potential short term effects as well, which have to be taken into consideration because “it’ll work out in 250 years” isn’t a very strong argument for clear-cutting rainforest.