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Interesting finds from Season 7!

Season 7 is up and running, and we’re already running into some interesting critters!

melanistic serval

No, that’s not a house cat wandering through the middle of the savanna. If you check out the gigantic ears, it appears that we’ve caught sight of a melanistic serval! Melanism involves a genetic mutation that causes the development of dark pigments in the skin, like a reverse albinism. For comparison, these guys normally look like this:

regular ol’ serval

According to our field assistant, Daniel, the serval population is booming in the Serengeti at the moment. Perhaps with so many animals, it’s not entirely unexpected to find one or two odd cases like this.

Let’s see what other interesting finds pop up in the latest season of Snapshot Serengeti!

Season 7

It’s been a long time coming, but it’s really and truly here.

First, we have to reiterate that Season 7 would never have been possible without your help. Last summer, our long-term National Science Foundation funding ran out and we were facing a gap in funding that could have closed down the camera survey forever. We launched the Save Snapshot Serengeti campaign to make sure that Season 7 happened — and because of your support, it did.   Thank you, again, for making this project possible in so many ways.

And now! The hard won photos of Season 7 are here.

Season 7 is a big one, running from May 2013 all the way through to the end of November. That’s 7 months! In that time, the long rains faded and the roads became dusty.  Stan, whose face you’ve seen so many times checking cams

Stan

began a master’s program in Dar es Salaam, and Norbert took over checking the cameras in his place.

Norbert!

Back in Minnesota, Margaret defended her dissertation and began a new post doc at Harvard working with the Phenocam project. I’ve been frantically analyzing data from Seasons 1-6 to finish my dissertation. And Meredith became the newest member of the Snapshot Serengeti team.

So, stop whatever you’re doing for a few minutes and go check out Season 7. Because really, whose day *isn’t* brightened by photos like this?

Teehee…warthog nose!

What we’ve seen so far, Part IV

Last week I wrote about using really simple approaches to interpret camera trap data. Doing so makes the cameras a really powerful tool that virtually any research team around the world can use to quickly survey an ecosystem.

Existing monitoring projects in Serengeti give us a really rare opportunity to actually validate our results from Snapshot Serengeti: we can compare what we’re seeing in the cameras to what we see, say, from radio-tracking collared lions, or to the number of buffalo and elephants counted during routine flight surveys.

Ingela scanning for lions from the roof of the car.

Ingela scanning for lions from the roof of the car.

One of the things we’ve been hoping to do with the cameras is to use them to understand where species are, and how those distributions change. As you know, I’ve struggled a bit with matching lion photographs to known lion ranging patterns. Lions like shade, and because of that, they are drawn to camera traps on lone, shady trees on the plains from miles and miles away.

But I’ve finally been able to compare camera trap captures to know distributions for other animals. Well, one other animal: giraffes.  From 2008-2010, another UMN graduate student, Megan Strauss, studied Serengeti giraffes and recorded where they were. By comparing her data with camera trap data, we can see that the cameras do okay.

The graph below compares camera trap captures to known densities of giraffes and lions. Each circle represents a camera trap; the bigger the circle, the more photos of giraffes (top row) or lions (bottom row). The background colors reflect known relative densities measured from long-term monitoring: green means more giraffes or lions; tan/white means fewer. For giraffes, on the whole, we get more giraffe photos in places that have more giraffes. That’s a good sign. The scatterplot visualizes the map in a different way, showing the number of photos on the y-axis vs. the known relative densities on the x-axis.

 

Fig7-8

What we see is that cameras work okay for giraffes, but not so much for lions. Again, I suspect that this has a lot to do with the fact that lions are incredibly heat stressed, and actively seek out shade (which they then sleep in for 20 hours!). But lions are pretty unique in their extreme need for shade, so cameras probably work better for most other species. We see the cameras working better for giraffes, which is a good sign.

We’ve got plans to explore this further. In fact, Season 7 will overlap with a wildebeest study that put GPS collars on a whole bunch of migratory wildebeest. For the first time, we’ll be able to compare really fine scale data on the wildebeest movements to the camera trap photos, and we can test even more precisely just how well the cameras work for tracking large-scale animal movements.  Exciting!

What we’ve seen so far, Part III

Over the last few weeks, I’ve shared some of our preliminary findings from Seasons 1-6 here  and here. As we’re still wrapping up the final stages of preparation for Season 7, I thought I’d continue in that vein.

One of the coolest things about camera traps is our ability to simultaneously monitor many different animal species all at once. This is a big deal. If we want to protect the world around us, we need to understand how it works. But the world is incredibly complex, and the dynamics of natural systems are driven by many different species interacting with many others. And since some of these critters roam for hundreds or thousands of miles, studying them is really hard.

I have for a while now been really excited about the ability of camera traps to help scientists study all of these different species all at once. But cameras are tricky, because turning those photographs into actual data on species isn’t always straightforward. Some species, for example, seem to really like cameras,

so we see them more often than we really should — meaning we might think there are more of that critter than there really are.  There are statistical approaches to deal with this kind of bias in the photos, but these statistics are really complex and time consuming.

This has actually sparked a bit of a debate among researchers who use camera traps. Researchers and conservationists have begun to advocate camera traps as a cost-effective, efficient, and accessible way to quickly survey many understudied, threatened ecosystems around the world. They argue that basic counting of photographs of different species is okay as a first pass to understand what animals are there and how many of them there are. And that requiring the use of the really complex stats might hinder our ability to quickly survey threatened ecosystems.

So, what do we do?  Are these simple counts of photographs actually any good? Or do we need to spend months turning them into more accurate numbers?

Snapshot Serengeti is really lucky in that many animals have been studied in Serengeti over the years. Meaning that unlike many camera trap surveys, we can actually check our data against a big pile of existing knowledge. In doing so, we can figure out what sorts of things cameras are good at and what they’re not.

Comparing the raw photographic capture rates of major Serengeti herbivores to their population sizes as estimated in the early 2000′s, we see that the cameras do an okay job of reflecting the relative abundance of different species. The scatterplot below shows the population sizes of 14 major herbivores estimated from Serengeti monitoring projects on the x-axis, and camera trap photograph rates of those herbivores on the y-axis. (We take the logarithm of the value for statistical reasons.) There are really more wildebeest than zebra than buffalo than eland, and we see these patterns in the number of photographs taken.

HerbivorePopComparison

Like we saw the other week, monthly captures shows that we can get a decent sense of how these relative abundances change through time.

pkslide

So, by comparing the camera trash photos to known data, we see that they do a pretty good job of sketching out some basics about the animals. But the relationship also isn’t perfect.

So, in the end, I think that our Snapshot Serengeti data suggests that cameras are a fantastic tool and that raw photographic capture rates can be used to quickly develop a rough understanding of new places, especially when researchers need to move quickly.  But to actually produce specific numbers, say, how many buffalo per square-km there are, we need to dive in to the more complicated statistics. And that’s okay.

A change of scene; jungles

I have just got back from a short (too short!) trip to Costa Rica. I wasn’t sure what to expect from this world famous eco destination but I decided to bring my camera trap with me on the off chance I would be given permission to set it up and capture something new, for me anyway.

My time was split between two distinct ecosystems, lowland rainforest of the Caribbean slope and dry forest of the Guanacaste/Nicoya peninsular. I stayed in protected areas and reserves so the chances of mammal activity was there, unfortunately I had only two or three days in each place which limited the chances of capturing anything somewhat. Hardly scientific I know but I was curious to see what might be out and about after I was tucked up in bed.

Now I am used to the African bushveld, a dry-ish, semi wooded, semi open grassland environment with a sandy substrate. It is easy to see where animals are frequently passing from trampled vegetation and tracks. Placing camera traps and getting results was not too hard. Costa Rica’s rainforests on the other hand was a challenge. The vegetation was thick, lush and resilient to animal passage and finding tracks in the dense leaf litter was impossible. Costa Rica’s dry forests where just as bad. It was the dry season and the trees had lost all their leaves…big leaves that made a thick carpet on the ground covering any tracks and trails.

Needless to say I did not get too many results but taking my time frame and lack of local knowledge into account it is amazing that I got anything. All the animals were at least new to me so gave me a great buzz.
Of the animals I captured peccaries, a type of pig where the most common in the rainforest followed by crab eating raccoons in the dry coastal forests. I captured 1 agouti, 1 paca, 1 probable grison devouring my camera and the most exciting of all an ocelot. Typically the ocelot was the very first capture on the first night. Staying at the Selva Verde lodge I had the help of the resident guide, Ivan and we placed the camera near to the river. I think he was just as thrilled as I was to get an ocelot despite their being the most common of the neo tropical cats.
I hope you enjoy this short video clip, you can see if you look closely that the ocelot is carrying something in its mouth, prey?

Find that nest!

You’ve got to check out this game:  http://nightjar.exeter.ac.uk/story/nightjar_game

Screen Shot 2014-03-16 at 3.08.08 PM

Scientists from the University of Exeter are trying to understand camouflage. Specifically, they want to understand how camouflage helps protect animals from being eaten for dinner, and they’re doing this by studying ground nesting birds in South Africa & Zambia.

Like Snapshot Serengeti, these guys use camera traps too, to figure out whose munching on birds and their nests. Unlike Snapshot Serengeti, however, they aren’t asking for help IDing the photos: instead, they’re asking for help figuring out how predators see, and how different types of camouflage work better or worse against predators with different types of vision.

Humans have trichromatic vision, meaning we have three different types of receptors (light sensitive cells in the eye) that can process color: red (longwave), green (mediumwave), and blue (shortwave). Some animals only have two receptor types and can only see one or two colors, whereas other animals have four, allowing them to see wavelengths such as infrared or ultraviolet that are invisible to people.  Thus, what camouflages eggs against one predator might not work so well against another predator.

What these researchers have done is create a game that mimics the vision of other predators. So you get to see the world through the eyes of either a genet cat (with dichromatic vision) or a vervet monkey (with trichromatic vision), and “hunt” for birds or their nests in a series of pictures. This helps scientists understand how perception changes among different animals, and how camouflage works against different perception types.

So go check it out! But don’t forget to come back and then help us classify Season 7! We’ll announce its debut on the blog soon!

 

What we’ve seen so far, cont’d.

Playing with data is one of the many things I love about research. Yes, it is super nerdy. I embrace that.

Last week I shared with you the various critters we’re getting to *see* in the Snapshot Serengeti data. Over 100,000 wildebeest photos! Over 4,000 lions! And the occasional really cool rarity like pangolins

Pangolin!

and rhinos.

Rhino!

But the photographs carry a lot more information than just simply what species was caught in the frame. For example, because the photos all have times recorded, we can see how the Serengeti changes through time.

This graph shows the number of daily pictures of wildebeest and buffalo, and how the daily capture rates change through the seasons. Each set of bars represents a different month, starting in July 2010. Wildebeest are in dark green, buffalo in light green. The y-axis is on a square-root scale, meaning that the top is kind of squished: the difference from 30-40 is smaller than the distance from 0-10. Otherwise, we’d either have to make the graph very very tall, or wouldn’t really be able to see the buffalo counts at all.

pkslide

Buffalo are captured more-or-less evenly across the different months. But the wildebeest show vast spikes in capture rates during the wet season. These spikes in numbers coincide with the migration, when the vast herds of wildebeest come sweeping through the study area.

Now, the number of photos doesn’t directly transfer into the number of wildebeest in the study area, and these aren’t changes in population size, but instead changes in distribution of the wildebeest. But it’s pretty cool that with something as simple as just the number of photographs, we can see these huge changes that accurately reflect what’s going on in the system.

What we’ve seen so far…

As we prepare to launch Season 7 (yes! it’s coming soon! stay tuned!), I thought I’d share with you some things we’ve seen in seasons 1-6.

Snapshot Serengeti is over a year old now, but the camera survey itself has been going on since 2010; you guys have helped us process three years of pictures to date!

First, of the >1.2 million capture events you’ve looked through, about two-thirds were empty. That’s a lot of pictures of grass!

Overall

But about 330,000 photos are of the wildlife we’re trying to study.  A *lot* of those photos are of wildebeest. From all the seasons so far, wildebeest made up just over 100,000 photos! That’s nearly a third of all non-empty images altogether.herbivores

We also get a lot of zebra and gazelle – both of which hang out with the wildebeest as they migrate across the study area. We also see a lot of buffalo, hartebeest, and warthog — all of which lions love to eat.

carnivores

We also get a surprising number of photos of the large carnivores. Nearly 5,000 hyena photos! And over 4,000 lion photos! (Granted, for lions, many of those photos are of them just lyin’ around.)

Curious what else? Check out the full breakdown below…

Preview of “Tables.xlsx”

Big Cat Wars

I’m in the process of writing up some *really* cool camera trap results from Seasons 1-6, and plan to share them here next week (as soon as I make them pretty). It would never have been possible without your guys’ help.  But in the meanwhile, this just aired again on TV, and thought you might enjoy a bit of a break! They talk about the camera traps a bit ~33 minutes in.

 

A day in the life of a camera

Okay, okay, it’s actually more like 2 months in the life of the camera, but I strung selected images together for site M08, Season 4, “roll 2.”

It’s actually pretty cool. It amazes me just how much information we get on even a single camera. We can see the migration moving through, the grass greening up, and even a spat between lions and hyenas!

My next task will be to string *all* of the images together for the season, but there are >2,000 of them, which my little computer couldn’t quite handle just yet.

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