Featured photo: A herd of sable antelopes graze in Gorongosa National Park, Mozambique. Credit to Michael Paredes.

For this week’s show, Justine Atkins, a graduate student in the Department of Ecology and Evolutionary Biology at Princeton, came in and told us all about animal decision making and the (lack of) science behind protected areas for conservation.

First, we went over Justine’s current research: she wants to make a computer model that predicts animal behavior given particular constraints. For example, what would a herd of antelope decide to do if we build a fence across their grazing lands? Or if we take land they use as a resource for farming or urbanization? Of course, understanding an antelope’s state of mind in order to predict such things is no easy task.

The initial step is to evaluate what factors influence antelopes to make various decisions in the first place. Might a skinnier antelope take more risks to eat in a bountiful field, even if there might be predators around? To get real data that might answer questions like this, researchers like Justine have to sedate wild animals and collar them with GPS transponders (a full-time job, until all thirty antelope are monitored and back with their herd!). Noting which animals are pregnant, or old or young, or skinny or well-fed, can give insight when Justine downloads their traveling paths and looks for patterns.

In the end, this massive amount of data (months of location data for 10-20 animals) will feed back into Justine’s code. Her simulation has a number of variables to consider that might affect each animal’s next move: hunger, danger, memory of the area, pregnancy… Until the simulation can be checked against real data, it’s hard to know how an antelope will weigh these considerations. But once the variables are weighted properly and the simulation can reproduce real transportation patterns from the wild, Justine can use the simulation to predict the antelopes’ response to future situations.

All of this goes hand in hand with conservation efforts. A researcher looking to protect endangered species might want to establish new protected areas, and should have an idea how the animals will react to such changes in their environment. Justine also has an interest in the science behind protected areas, particularly for evaluating their effectiveness. How do we know that a new national park has really helped the biodiversity within? Measuring its success is a difficult problem, especially since there are lots of fluctuations in nature that might confuse the study. Plus, in science we usually use control groups, so that a parcel of land that had protection should be compared against a similar parcel that was left in its original state. This type of research is rarely done in designating new protected areas.

Justine went into detail with this problem of impact evaluation for protected areas in a blog post on Highwire Earth (which comes up a lot on our show!). She brought up a few examples of well-researched protected areas, like La Selva in Costa Rica. There, the scientists put numbers to the state of each acre of rainforest they protected: some untouched, some reduced to farmland a hundred years ago, some just returned to forest within the last decade. Comparisons between these areas can lead to insight on re-development of forest after humans have intervened. Similarly, the savanna biosphere in Gorongosa National Park in Mozambique, where Justine carries out her data-taking on wild antelope populations, is recovering from a period of rampant poaching during a recent civil war. Ecologists there have the opportunity to study animal bounce-back after such a shock.

After the interview, Stevie brought up a viral GIF of Jose Ramirez running to second base. On his way, he loses his helmet, kicks it wildly as he runs, and it flies off-screen–only to come back and hit him in the head as he slides into the base. WIRED and These Vibes are here to tell you that physics didn’t break to turn Jose’s helmet into a boomerang. The effect is a trick of conserved momentum and of a panning camera. Since Jose was running when he kicked the helmet, it actually went straight up in his frame of reference, but kept moving forward at the same speed as the baseball player’s sprint. And the helmet appears to dip backward because the camera is moving sideways fast enough to trick you. In the end, it’s a bit of slapstick that actually makes physical sense.

We closed out the show with a quick discussion of chaos theory. In physics, chaos means something specific: two systems begin in almost the same scenario, but after a while they look totally different. This happens in the weather, since a Tuesday that turns into a Saturday thunderstorm isn’t much different at all from a Tuesday followed by a sunny weekend. Tiny fluctuations in the atmosphere end up foiling our weather prediction: it turns out we can’t do better than about a week out, even if we knew the temperature and pressure all over the globe. Other chaotic systems include three bodies in space or a double pendulum. This is a fascinating topic that we only just started to dig in to, so keep looking if you’re interested!

As always, the playlist is here or at WPRB.