So those of you who have read my blogs are probably used to hearing me bemoaning the fact that I am no longer in Africa but am back in France whilst I am studying towards a degree. I can’t really complain, life is still good, but the European wildlife feels a bit lacking when you have been used to the mega fauna of Africa.
I have however continued to put out my camera traps in order to survey my property and the surrounding countryside and it’s busier than you would think. Badgers, foxes, hares, otters, roe deer, squirrels, stone martens and wild boar make regular contact with my paltry two cameras. Not really enough camera power to base a study on but interesting all the same.
The highlight has been the discovery that one little favourite of mine from Africa has followed me to France; the genet. Yes the common genet (Genetta genetta) lives in France and I am ecstatic to say right by my house too.
It seems no-one really knows how they got here but it was probably something to do with the Romans centuries ago being brought over from the Magreb region of North Africa. They are now naturalised animals in Spain, Portugal and France. Refreshingly for an introduced animal they are not invasive and have little impact if any on the native wildlife, so I can go on loving them with a clear conscience.
Here are a couple of shots of genets in France for your enjoyment!
As Meredith posted the other day, one of our camera traps caught a melanistic serval. Melanism is known across a broad variety of animals, but is particularly prevalent in the cat family. Of 37 known species of cat, at least 13 species have melanistic individuals: the domestic cat, the jungle cat, the leopard, the jaguar, the bobcat, Geoffroy’s cat, the kodkod, the oncilla, the colocolo, the jaguarundi, the Asian golden cat, the marbled cat, and the serval.
Why some individuals are melanistic and why cats are particularly prone to melanism is still a bit of a mystery. It is generally thought that melanism is maladaptive – that is, that individuals with melanism are at a disadvantage because they stand out more than normally colored individuals and so are more likely to be targets of predators and competitors. The consequence is that in populations with a lot of melanism, there ought to be some sort of advantage to offset the disadvantage.
One possible explanation for melanism is that cats’ black fur helped keep them warm at higher elevations by absorbing more sunlight. This idea came from the fact that many cat populations with high rates of melanism are found at higher elevations. More recently, there have been studies suggesting that melanistic individuals are more resistant to disease.
There’s not a lot of literature on melanistic servals. But I did find an article in the Journal of East African Natural History that listed the known locations of melanistic serval populations in East Africa. Interestingly, the four main populations with melanism are all highland locations: Mt. Kenya and the nearby Aberdare highlands in Kenya and Mt. Kilimanjaro and North Pare Mountains in Tanzania. All of these are in the general geographic region of Serengeti National Park, so it’s perhaps not too surprising that melanistic servals are there too. What is unusual is that the Serengeti is not a highland.
Our long-term Serengeti experts, with their decades of experience in the Serengeti, are surprised by the melanistic serval snapped by our cameras. David Bygott says that he’s never heard of a melanistic serval in the Serengeti, and Craig Packer says that while he’s seen melanistic individuals of other animals up on the rim of Ngorongoro Crater (a highland), he’s never seen a melanistic serval anywhere. So this Snapshot Serengeti image is likely the only documented evidence of melanistic serval in the Serengeti.
As I’m writing up my dissertation (ahh!), I’ve been geeking out with graphs and statistics (and the beloved/hated stats program R). I thought I’d share a cool little tidbit.
Full disclosure: this is just a bit of an expansion on something I posted back in March about how well the camera traps reflect known densities. Basically, as camera traps become more popular, researchers are increasingly looking for simple analytical techniques that can allow them to rapidly process data. Using the raw number of photographs or animals counted is pretty straightforward, but is risky because not all animals are equally “detectable”: some animals behave in ways that make them more likely to be seen than other animals. There are a lot of more complex methods out there to deal with these detectability issues, and they work really well — but they are really complex and take a long time to work out. So there’s a fair amount of ongoing debate about whether or not raw capture rates should ever be used even for quick and dirty rapid assessments of an area.
Since the Serengeti has a lot of other long term monitoring, we were able to compare camera trap capture rates (# of photographs weighted by group size) to actual population sizes for 17 different herbivores. Now, it’s not perfect — the “known” population sizes reflect herbivore numbers in the whole park, and we only cover a small fraction of the park. But from the graph below, you’ll see we did pretty well.
Actual herbivore densities (as estimated from long-term monitoring) are given on the x-axis, and the # photographic captures from our camera survey are on the y-axis. Each species is in a different color (migratory animals are in gray-scale). Some of the species had multiple population estimates produced from different monitoring projects — those are represented by all the smaller dots, and connected by a line for each species. We took the average population estimate for each species (bigger dots).
We see a very strong positive relationship between our photos and actual population sizes: we get more photos for species that are more abundant. Which is good! Really good! The dashed line shows the relationship between our capture rates and actual densities for all species. We wanted to make sure, however, that this relationship wasn’t totally dependent on the huge influx of wildebeest and zebra and gazelle — so we ran the same analysis without them. The black line shows that relationship. It’s still there, it’s still strong, and it’s still statistically significant.
Now, the relationship isn’t perfect. Some species fall above the line, and some below the line. For example, reedbuck and topi fall below the line – meaning that given how many topi really live in Serengeti, we should have gotten more pictures. This might be because topi mostly live in the northern and western parts of Serengeti, so we’re just capturing the edge of their range. And reedbuck? This might be a detectability issue — they tend to hide in thickets and so might not pass in front of cameras as often as animals that wander a little more actively.
Ultimately, however, we see that the cameras do a good overall job of catching more photos of more abundant species. Even though it’s not perfect, it seems that raw capture rates give us a pretty good quick look at a system.
I thought I would share these video clips from my camera trap with you. During my research using camera traps in South Africa I mostly used the picture mode but in the early days when I was trying to figure out what the camera trap was capable of and what was most valuable from my research point of view I messed around with the video option.
From my research perspective it wasn’t that great, I found that the camera was slower to trigger in video mode and so particularly at night I was left with lots of footage of nothing. But from pure interest value it sometimes proved very interesting.
On this occasion I had set up the camera on a sand road hoping to capture the leopard who frequently passed that way leaving its pug marks for all to see. I was pretty sure of capturing the leopard. What I didn’t bank on was getting a full 17 minutes of footage of two giraffe battling it out. Nor was I expecting the unique perspective from which the camera shot the footage. Oh and the leopard never showed. Typical!
I hope you enjoy the following three short clips.
In news not quite as exciting as Ali’s (congratulations again!), I have just gotten word from the Tanzanian research institute that the proposal I submitted for summer research have been approved! It looks like I’ll be heading out to Dar es Salaam and Arusha in the next three weeks to get the rest of my permits sorted out, and then head into the field immediately afterward. Definitely looking forward to seeing this amazing system first-hand — I’m sure it will be a surreal experience, after becoming so familiar with the animals and landscape through the camera trap images. Added bonus: I get to leave Minnesota, where it is still snowing. Hurrah!
Back in October, I wrote about how a grant proposal was turning me into a zombie.
Well, much to my surprise, turns out that my foray into the world of the walking dead was worth the effort. I’ve just heard that the National Science Foundation does, indeed, want to send me to South Africa to carry out this research!
Basically, I’m interested in how the other big carnivores (hyenas, leopards, cheetahs, and wild dogs) manage to live with lions. And I think that one of the keys to their coexistence has to do with how the other carnivores distribute themselves across the landscape to avoid being killed or harassed by lions. Do they avoid huge tracts of land and lose access to the valuable resources within? Or are they able to fine-tune their behavior and still use those areas without getting into trouble?
As you know, I’m using the camera traps to try and figure out these patterns of habitat use by the major carnivores. But that still just tells me what they do in a place (the Serengeti) where there are lions, and I don’t know if the lions are directly causing these patterns. I can’t, for obvious reasons, do an experiment where I take out all the lions and see if the rest of the animals change their behaviors, which would help me identify such a causal relationship.
But in South Africa, there are two virtually identical reserves — they have the same habitat, the same prey animals, and the same carnivores…except that one has lions and one does not. These reserves are right next to each other and surrounded by fencing. So they are pretty much the perfect experimental system where I can actually answer whether or not the patterns we see in predator behavior are caused by lions. What’s even better is that there are already ongoing research projects there that are running camera trap surveys very similar to Snapshot Serengeti. So most of my work will be doing some measurements of the vegetation and working with the researchers in South Africa to compile their data in a way that we can draw these comparisons.
It’s going to be a *lot* of computer work with a *little* bit of getting out into the bush, but the questions are so cool and the ability to effectively isolate the effect of a single top predator (lions) in a natural ecosystem is so rare, that I couldn’t be more excited about it.
Season 7 is up and running, and we’re already running into some interesting critters!
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:
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!
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
began a master’s program in Dar es Salaam, and Norbert took over checking the cameras in his place.
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?
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.
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.
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!
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.
Like we saw the other week, monthly captures shows that we can get a decent sense of how these relative abundances change through time.
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.