Nature’s Supply And Demand Problem

Algorithms Ecosystems Wildlife

“Supply and demand” is a phrase that’s more commonly associated with economics and business than with the environment. And yet, when we think about it – Nature provides several services that we take for granted… until they aren’t there anymore. Clean air for example – natural systems have filtered and purified air around cities and homes for many years, until the output from our cities becomes too much for the natural system and then we start noticing the smog and pollution. Or flood control – mangroves in the coastal areas of the tropics provide buffers against storm surges and flooding from hurricanes, until they are cut down for development and then we are faced with multi-million dollar damages from a storm.

 

Several ecologists and economists have worked together to try and figure out how best to quantify or price the services that natural systems provide, often called ecosystem services. But what happens as the environment changes, the climate warms and several ecosystems are threatened? Will they still be able to provide the services that people have come to rely on or not?

 

That question is what drove researchers in Australia to build a new algorithm that looked at the tradeoffs and threats to ecosystems and the services they provide. The algorithm is built to consider two thresholds – 1) when the demand on the ecosystem exceeds its ability to perform the service it’s doing or 2) when the ecosystem is in danger of becoming extinct and therefore not being able to provide the service at all.

 

An example of the first scenario would be if a forest was supplying wood for biomass to a community but the population expanded to the point where there were more trees felled than were growing. Then the ability of the forest to supply energy is severely impacted – and the only way forward would be to either improve the technology used so that less wood is needed or to reduce the population that can access the forest’s resources.

 

An example of the second scenario would be if a community was dependent on seals for food and fuel – like the Inuit in the Artic region. As the climate changes and the Artic ecosystem is threatened with extinction, it would become increasingly difficult for the Inuit to survive in their traditional lifestyle.

 

So what would such an algorithm be useful for? One possibility is when companies and development agencies start looking at incorporating the environment and ecosystem services into any new development plan. For example, let’s say a factory is going to be built near a community where water is not always available and the factory intends to pump groundwater to meet its requirements. What if a conversation was started between the community and the factory at the beginning – where the requirements of each are clearly described, the benefits that accrue to each are evaluated, and the end goal is to meet both the factory and the community needs without depleting the water supply? The end result of such a discussion and the goals being input into the algorithm could be a system where water is recycled after use, or where a rainwater harvesting system is incorporated, or where both the community and the factory decide that they can reduce the amount of water they need.

 

Now contrast this scenario with the business-as-usual one. The factory comes in, groundwater is often over extracted, the community gets upset and protests start near the factory, there’s bad press and bad relationships between the two and the end result is that neither the community nor the factory nor the environment win.

 

That’s where algorithms like this and data science can come in handy. They provide a framework for organizations, corporations and governments around the world to start looking at and characterizing how the environment is benefiting them, the threats they are currently facing to these benefits and the actions that can be taken to restore or improve the ecosystem. It’s something that would likely form the basis of an analytical tool for a data scientist working in these organizations, even if it’s not something that is commercially available just yet.


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