Measuring the Human Footprint in Protected Areas

This past fall I was lucky enough to take Dr. Dan Runfola’s Data Driven Decision-making course. I learned more than I ever wanted to know about Monte Carlo simulations in excel, measuring and including uncertainty in our analyses, and how to work with policymakers. By the end of the semester we were paired up with organizations that fell within our wheelhouse: one group examined judicial efficacy in Liberia, another worked with Save the Children, and I was set up with Dr. Healy Hamilton from NatureServe.

Healy’s team has done some truly impressive work with conservation data in the US, and it was my task to look at vulnerable protected areas. Using Healy’s human footprint data (over 20 indicators compiled into 3 main categories of infrastructure, transportation, and land use) I selected the top 10 HUCs that contain US protected areas that are vulnerable to human disturbance. In addition, while NatureServe’s data is incredibly robust, it is currently under review for publication–why the analysis below is restricted to the Western half of the US–and it’s unclear to me who will have access to this kind of data. This begged the question: what could serve as a relatively simple proxy for this robust human footprint dataset?

human footprint
According to Dr. Hamilton’s dataset, this map highlights the HUCs that contain protected areas most vulnerable to human disturbance.
nighttimelights
The first possible proxy for human disturbance: nighttime lights. This shows the HUCs with protected areas most vulnerable to this proxy of human disturbance.
roaddensti
The second proxy: road density.
Final Map
Finally, the comparison of the three metrics. Only 1 HUC contained all three.

Unsurprisingly, there was no strong overlap between the proxies and Healy’s data–something I attribute to the inclusion of the variable land cover in Healy’s dataset–and I wouldn’t argue that nighttime lights or road density are appropriate proxies. However, they do have some value. Using these indicators offer more specific instructions to policymakers: the HUCs that have protected areas suffering with high nighttime lights could likely use a public awareness campaign that reduces light pollution (aside: did you know that light pollution is a major contributor to bird death?). Similarly for road density, protected areas in these HUCs could maybe use habitat corridors over/around nearby roads. I’m just spitballing here.

I would say that the NatureServe data would be incredibly powerful to hand over to policymakers when they ask where the most vulnerable protected areas are located, whereas the proxies might be better for making specific policy recommendations.

Find the full brief plus the analysis step-by-step, here: Final_Brief

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Measuring the Human Footprint in Protected Areas

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