I've already written in the past about Microsoft's efforts to help catalog endangered species. It turns out that's just the tip of the iceberg for how the software developer is applying its massive data analytics and business intelligence capabilities to helping solve environmental questions and challenges.
For the past two years, Microsoft has been working with the United Nations Environment Program World Conservation Monitoring Center (UNEP-WCMC) to create computer models that "mimic the physics and chemistry of the planet's land, ocean, and atmosphere," Josh Henretig, who is responsible for Microsoft's global environmental sustainability strategy, wrote on the company's Green Blog.
Models of these sort are used pretty commonly and extensively for mapping climate change and predicting potential effects. Now, scientists and researchers are looking to apply the same principles to marine and terrestrial ecosystems with something called general ecosystem models (GEMs).
Here's how the UNEP-WCMC explains it:
"Scientists from Microsoft Research and UNEP-WCMC argue that GEMs could capture the broad-scale structure and function of any ecosystem in the world by simulating processes — including feeding, reproduction, and death--that drives the distribution and abundance of organisms within the ecosystems. Such an approach could provide a way to base future conservation policy on an understanding of how ecosystems actually work."
So, for example, scientists could study the systems behind African savannas to have a better sense of what might happen to them over time, especially in the face of variable factors such as the effects of poaching of certain animal populations or water shortages triggered by climate change.
Microsoft Research and UNEP-WCMC already have built a prototype for one of these systems, called the Madingley Model, which is using data on carbon flows. They have published some of their findings in an article in the journal Nature. And they are encouraging other scientific organizations to participate in building far more elaborate models.
"One challenge is that while some of the data needed to create an effective GEM has already been collected and stored away in research institutions, more data is needed," wrote Henretig. "A new major data-gathering program would be expensive, so supporters of GEMs are calling on governments around the world to support programs that manage large-scale collection of ecological and climate data."
The ultimate hope is that these models will eventually be accurate enough and detailed enough to guide meaningful conservation policy.