Archive | December 2012

We need an ‘I don’t know’ button!

Okay, okay. I hear you. I know it’s really frustrating when you get an image with a partial flank or a far away beast or maybe just an ear tip. I recognize that you can’t tell for sure what that animal is. But part of why people are better at this sort of identification process than computers is that you can figure out partial information; you can narrow down your guess. That partial flank has short brown hair with no stripes or bars. And it’s tall enough that you can rule out all the short critters. Well, now you’ve really narrowed it down quite a lot. Can you be sure it’s a wildebeest and not a buffalo? No. But by taking a good guess, you’ve provided us with real, solid information.

We show each image to multiple people. Based on how much the first several people agree, we may show the image to many more people. And when we take everyone’s identifications into account, we get the right answer. Let me show you some examples to make this clearer. Here’s an easy one:

Giraffe

And if we look at how this got classified, we’re not surprised:

data1

I don’t even have to look at the picture. If you hid it from me and only gave me the data, I would tell you that I am 100% certain that there is one moving giraffe in that image.

Okay, let’s take a harder image and its classifications:

buffalo

data2

This image is, in fact, of buffalo – at least the one on the foreground is, and it’s reasonable to assume the others are, too. Our algorithm would also conclude from the data table that this image is almost certainly of buffalo – 63% of classifiers agreed on that, and the other three classifications are ones that are easily confused with buffalo. We can also figure out from the data you’ve provided us that the buffalo are likely eating and moving, and that there is one obvious buffalo and another 2 or 3 ones that are harder to tell.

My point in showing you this example is that even with fairly difficult images, you (as a group) get it right! If you (personally) mess up an image here or there, it’s no big deal. If you’re having trouble deciding between two animals, pick one – you’ll probably be right.

Now what if we had allowed people to have an ‘I don’t know’ button for this last image? I bet that half of them would have pressed, ‘I don’t know.’ We’d be left with just 4 identifications and would need to send out this hard image to even more people. Then half of those people would click ‘I don’t know’ and we’d have to send it out to more people. You see where I’m going with this? An ‘I don’t know’ button would guarantee that you would get many, many more annoying, frustrating, and difficult images because other people would have clicked ‘I don’t know.’ When we don’t have an ‘I don’t know’ button, you give us some information about the image, and that information allows us to figure out each image faster – even the difficult ones.

“Fine, fine,” you might be saying. “But seriously, some of those images are impossible. Don’t you want to know that?”

Well, yes, we do want to know that. But it turns out that when you guess an animal and press “identify” on an impossible image, you do tell us that. Or, rather, as a group, you do. Let’s look at one:

little-critter

Now I freely admit that it is impossible to accurately identify this animal. What do you guys say? Well…

data3

Right. So there is one animal moving. And the guesses as to what that animal is are all over the place. So we don’t know. But wait! We do know a little; all those guesses are of small animals, so we can conclude that there is one small animal moving. Is that useful to our research? Maybe. If we’re looking at hyena and leopard ranging patterns, for example, we know whatever animal got caught in this image is not one we have to worry about.

So, yes, I know you’d love to have an ‘I don’t know’ button. I, myself, have volunteered on other Zooniverse projects and have wished to be able to say that I really can’t tell what kind of galaxy that is or what type of cyclone I’m looking at. But in the end, not having that button there means that you get fewer of the annoying, difficult images, and also that we get the right answers, even for impossible images.

So go ahead. Make a guess on that tough one. We’ll thank you.

The things that live inside…the cameras

Whenever I rock up to a camera trap, I sort of hold my breath and brace myself for what I’m going to find.  Sometimes I find nothing — elephants have tossed the camera off the tree and into the green grassy oblivion, or hyenas have left dribblings of mangled plastic and tooth-dented batteries — but stories about the never ending crusade to protect the cameras from overenthusiastic large mammals will come another day.  Today is about the wildlife that try to make my cameras home.

I’m always a little surprised at what I find. Geckos love to lay their eggs in the metal cases, though they and the skinks tend to act rather molested when I disturb them.

a skink!

a skink!

Other inhabitants are a bit slower to react, like this caterpillar:

one of the cuter inhabitants...

one of the cuter inhabitants…

And then there are some mysteries…

IMG_5588

Some sort of spider eggs, perhaps?

IMG_5470

I have no idea. Any suggestions welcome.

IMG_6579

Something has made a mysterious home of rolled up leaves.

The only thing that I really can’t bear is the ants.  Don’t get me wrong, ants are cool – and they do *really* cool things – but they also bite. And when they’ve turned a camera into their home (as in the photo below —  those white bits are eggs or larvae), they aren’t particularly welcoming to researchers. I’ll try to get som clearer photos this field season – because I guarantee you, there will be many, many ants to come.

Ants.

Ants.

Lions, hyenas, and leopards, oh my.

Craig (my adviser and the Director of the Lion Project) sometimes jokes that I wandered into his office looking to study tigers. It’s actually sort of true.  I had been at the University of Minnesota to interview with a tiger researcher – but fell in love with the science that Craig’s team was conducting. Six months later I became the newest addition to the Lion Lab.

As part of the Lion Lab, my dissertation research focuses on how lions coexist with other large carnivores – hyenas, leopards, and cheetahs. Understanding how species coexist is a really big question in ecology. When two species eat the same thing, the species that eats (& reproduces) faster can exclude the slower species from that area. A lot of ecology is devoted to understanding the conditions that allow for coexistence in the face of such competition.  The natural world is an incredibly diverse place, and it turns out the plants and animals have all sorts of strategies to survive together – though we’ll have to dive into those details another day.

Carnivores throw a bit of a wrench into our understanding of coexistence – even when they don’t eat exactly the same prey, they harass each other, steal food from each other, and even kill each other – and these aggressive interactions can result in dramatic suppression or even complete exclusion of certain species.  For example, there’s a fair bit of evidence that wild dogs have a tough time surviving in areas with lots of lions and hyenas – not because lions and hyenas kill wild dogs, but because they steal food from them.  Since wild dogs expend so much energy hunting, they simply can’t afford to lose those calories to scavengers. These patterns aren’t actually unique to large carnivores – a lot of animals, from bugs to birds, interact this way. However, since carnivores range over such large areas, it can be challenging to understand their dynamics.

That’s where the camera traps come in.  The long term lion research project provides incredible amounts of detailed data on what lions do, where they are, and how successful they are at reproducing.  By adding the camera survey on top of the lion study area, I can collect information about the other carnivore species and integrate it with the detailed lion data to ask bigger questions than could be answered with one dataset alone.  Unfortunately, there aren’t any wild dogs left within the study area, but I can still investigate how lions coexist with leopards, cheetahs and hyenas.  It’s a bit gruesome when you get down to it — lions tend to dominate all the other species when it comes to one-on-one interactions, stealing their food or even just killing them for no apparent reason. For example, lions kill somewhere between 25-55% of cheetah cubs! And you can see here Stan’s photos of lions just killing…and leaving…a leopard.

Lions chasing…

...and catching...

…and catching…

...and leaving...a leopard

…and leaving…a leopard

Lions will also kill hyenas, but enough hyenas can be a pretty solid threat to lions – able to steal carcasses or kill their cubs.  Leopards sometimes kill and eat lion cubs.  We don’t yet know if hyenas and leopards do this at a rate that actually hurts lions in the long-term, but we’re hoping to find out.

One of the key things I’m trying to find out (with a lot of green coffee and evening sessions) is how these species use their habitat with respect to each other.  Research in other ecosystems shows that smaller carnivores (those that usually lose a fight) can get pushed out of large areas, existing sort of in the ‘no-man’s land’ between top carnivore territories – and when this happens, their numbers can plummet.  However, if the smaller carnivore can just avoid the larger one within its territory, they might be able to coexist.  A lot of this depends on the habitat complexity – for example, in open areas, it’s harder for the smaller guy to hide.

The camera traps let me evaluate these different patterns of avoidance to understand how lions, hyenas, leopards, and cheetahs all coexist in Serengeti National Park.  Once we understand their dynamics in Serengeti, we can hopefully understand why they do or don’t coexist elsewhere.  It’s a pretty cool science question – and it’s also an amazing adventure.  I head back to Serengeti this January for my final field season, and am looking forward to sharing the adventure with you on this blog.

Welcome to Snapshot Serengeti

Hi! And welcome to Snapshot Serengeti. We are all incredibly excited to be working with you to turn photographs into scientific discoveries. You might be wondering what this is all about, so let me start with some introductions. This is Ali:

ali_Fabio

Ali is a researcher at the University of Minnesota. She studies the big carnivores (lions, hyena, cheetahs, and leopards) in the Serengeti. Every year she flies to Tanzania, loads up on supplies in Arusha, and then drives for a day – mostly on dirt roads – out into Serengeti National Park.

TanzaniaSerengetiNP

This is Craig:
Craig-Packer-Tanzania-3

Craig is a professor at the University of Minnesota and Ali’s advisor. He runs the Lion Research Center has been studying lions out in the Serengeti for decades. He has radio collars on lions in many prides, which allows him to keep track of lots individual lions over many years.

This is Daniel:

DanielRosengren

And this is Stan:

Stan

They are field assistants who work for Craig out in the Serengeti. Daniel is responsible for driving around and finding lions, while taking pictures of them and recording lots of information about what he sees. Stan is responsible for going out to the camera traps, making sure they’re still working fine, and changing the cameras’ memory cards when they fill up. Daniel and Stan live in Serengeti year-round at Lion House, where facilities are basic, but the scenery is amazing.

LionHouse

When Ali goes out the Serengeti, she stays at Lion House, too. Once she’s there, she makes observations that help her understand the big carnivores. A couple years ago, she installed a bunch of camera traps so she could see where the carnivores roamed when she wasn’t present. The cameras worked really well and the images were so useful that she installed some more. Now there are 225 of these cameras automatically taking pictures out the Serengeti!

camera

My name is Margaret. This is me:

Margaret2
Like Ali, I’m a researcher at the University of Minnesota, and Craig is my advisor, too. Ali became inundated with the images the camera traps produced – a million per year! I have a reputation around here as a computer fundi – a Swahili word that translates as ‘master’ or ‘expert’ – and Ali asked me if there was a way to automate the process of turning images into data. See, the images by themselves aren’t that useful for research; Ali needs to know what species are in the pictures so she can do her analyses. For example, if she knows which images contain wildebeest and zebra, she can use that data put together a map that shows their density across the landscape. (The size of the circles show how many wildebeest and zebra there are in various places — bigger circles mean more wildebeest and zebra.)

animalMapUnfortunately, I had to tell her that computers aren’t that good yet. They can’t yet reliably pick out objects from a picture, except under very controlled situations. But human eyes are remarkable in their ability to find objects in images. As I started looking through Ali’s images, I was blown away by how beautiful many of them are. And I wondered if we could ask for help from people. Lots of people. Hundreds. Thousands. So we started to think about how to do that.

The end result is Snapshot Serengeti, a collaboration with Zooniverse. We’d like to ask you to help us turn all these pictures from the Serengeti into scientific data by identifying what animals are in the images and what they’re doing. And in this blog, we will keep you updated on how the project is progressing, share cool information about the Serengeti and African wildlife, as well as hopefully answer a lot of questions you may have about animal behavior, ecology, and science in general.

So, check out the camera trap images. Tell us what you see in them. And let us know if you have questions. Thanks! You can get started by clicking here.

Coming soon…

…watch this space later today.