Tag Archive | detection

The trouble with shade

Who knew that shade could be so problematic? A couple of weeks ago, I wrote about how shade seems to be my biggest obstacle in reconciling how the cameras see the world vs. what is actually going on. My job is to figure out how to make things right.

To start with, the camera traps are up on trees.  Mostly. As you know, the cameras are on a rough grid layout – 225 grid cells, each 5km2 (2.236km on each side)  — covering a total of 1,125 km2 of Serengeti’s center. This kind of design makes sure that we are covering enough of the landscape to capture the bigger picture of animal distributions and movements. Each camera is roughly at the center of each grid cell – on the closes suitable tree to that center point. Some trees are big and shady; some are small and spindly. In the woodlands, there are trees everywhere; on the plains, the camera-trap tree can be the only tree for miles.  And sometimes there are no trees at all, and here the cameras get put up on metal poles.

IMG_8301 SitePhotoPoles

These different habitats are important to capture. I think that animals might behave very differently in areas with lots of trees than they do in areas with very few trees. When it comes to the aggressive interactions between carnivores, for example, trees, shrubs, and tall grass provide great hiding places for the smaller species. It’s like trying to hide from someone you don’t like in an empty room vs. in a really huge, crowded shopping mall.


It’s a lot harder to hide from lions here

...than here

…than here

The problem is that camera traps work better in some habitats than others – at least for certain species. Say you are a huge, muscle-bound lion. Even standing is tiring in the Serengeti heat, and you spend your days breathing heavily even at rest. You like shade. A lot. If you are out in the open plains, a single shade tree will stick out for miles, and you’ll probably work your way to it. Chances are, that tree has a camera. In the woodlands, though, there are lots of trees. And the camera trap could be on any one of them. So even if you’re searching for shade, the chances of you walking past the camera trap in the woodland are far smaller – just because there are so many trees to choose from.

Here’s a map of the study area – green shows more densely wooded areas, whereas yellow marks the plains. Camera traps that have captured lions are shown with circles; the bigger the circle, the more lions were seen at that trap. I know for a fact that there are more lions in the northern half of that map than in the southern half, but the lions out on the plains seem to really like getting their picture taken!


The pattern looks a little better at night than in the day, but it’s not perfect. So perhaps shade isn’t the only thing affecting how these cameras “see” lions in different habitats.

As depressing as this problem seems at first glance, I’m optimistic that we can solve it (enter Kibumbu’s new GPS collar!), but those methods are material for another day. In the meanwhile, what else do you think might be going on that attracts lions, or other animals to trees, besides shade?


Detecting the right number of animals

This past spring, four seniors in the University of Minnesota’s Department of Fisheries, Wildlife, and Conservation Biology took a class called “Analysis of Populations,” taught by Professor Todd Arnold. Layne Warner, Samantha Helle, Rachel Leuthard, and Jessica Bass decided to use Snapshot Serengeti data for their major project in the course.

Their main question was to ask whether the Snapshot Serengeti images are giving us good information about the number of animals in each picture. If you’ve been reading the blog for a while, you know that I’ve been exploring whether it’s possible to correctly identify the species in each picture, but I haven’t yet looked at how well we do with the actual number of animals. So I’m really excited about their project and their results.

Since the semester is winding up, I thought we’d try something that some other Zooniverse projects have done: a video chat*. So here I am talking with Layne, Samantha, and Rachel (Jessica couldn’t make it) about their project. And Ali just got back to Minnesota from Serengeti, so she joined in, too.

Here are examples of the four types of covariates (i.e. potential problems) that the team looked at: Herd, Distance, Period, Vegetation

Herd: animals are hard to count because they are in groups


Distance: animals are hard to count because they are very close to or very far from the camera


Period: animals are hard to count because of the time of day


Vegetation: animals are hard to count because of surrounding vegetation



* This was our first foray into video, so please excuse the wobbly camera and audio problems. We’ll try to do better next time…