Notes from the field
Note: Meredith wrote this blog post, but is having internet problems in Africa, so I am posting it on her behalf.
Pole sana on the lack of recent field updates – it’s been a busy week or two and I’ve traveled halfway across Africa in the meanwhile! Sad to say, I’ve left Serengeti behind for now. I was able to set up almost all of the replacement cameras I brought down with me and completed three new rounds for my playback experiments. I then took a few days off and spent my birthday traveling around in Ethiopia, soaking in some history and culture (and eating really excellent food!). It’s nice to have a break from constant science every once in a while. I went around what is known as the “Northern Circuit” and visited the four historic cities of Gondar, Lalibella, Aksum, and Bahir Dar. I got to visit island monasteries, rock-hewn churches, the palace of the Queen of Sheba, and even made a trip to the church purported to be where the True Ark of the Covenant is kept! Have to say, the trip made me feel very “Indiana Jones”, right up until the point where I got ill from drinking the water…
After a week in Ethiopia, I flew down to Johannesburg, South Africa, to meet up with Craig and another graduate student we’re working with, Natalia. Natalia is interested in cognition and has been testing the creative problem solving and impulse control of different kinds of carnivores. We’ve spent the last few days at a reserve outside of Pretoria called Dinokeng, run by Kevin “the Lion Whisperer” Richardson. Kevin maintains a park with dozens of semi-captive lions, leopards, and hyenas which Natalia can work with for her intelligence experiments. While Natalia has been busy with her research, I’ve been putting together a rig that will enable me to examine herbivore responses to four predator species: cheetah, wild dog, lion, and hyena. Two of these predators (lion and cheetah) hunt by sneaking up on their prey, whereas the others (wild dog and hyena) rely on endurance to run prey down. I’m looking to see whether prey respond to each species of predator differently, or whether there are consistent differences in anti-predatory response by predator hunting type. I’ll be simulating predator encounters because it would be incredibly difficult to observe a sufficient number of actual encounters in the wild. As soon as I find a good internet connection, I’ll post pictures of just exactly how I plan on doing this — it’s pretty great, and I don’t want to ruin the surprise!
Just this morning, the three of us packed up all of our gear and took a small plane out of Pretoria up to South Africa’s Northern Cape province. We’ll be spending the next three to four weeks up here in the Kalahari conducting our experiments. In addition to looking at anti-predator responses, I’ll be helping to set up a NEW camera trap grid (perhaps Snapshot Serengeti will be joined by Kalahari Cameras sometime in the near future…?). Now that we’re back in action, more updates soon!
Check out Mammal March Madness!
For some reason I missed this in 2013 and 2014. Maybe it was because I was finishing up my dissertation the first time and then recovering from a cross-country move the second time. But now I am totally excited about 2015’s
Mammal March Madness
What is Mammal March Madness? I’ll let organizer Katie Hinde explain:
In honor of the NCAA College Basketball March Madness Championship Tournament, Mammals Suck is featuring *simulated* combat competition among mammals. … Battle outcome is a function of the two species’ attributes within the battle environment. Attributes considered in calculating battle outcome include temperament, weaponry, armor, body mass, fight style, and other fun facts that are relevant to the outcome.
As a spectator to Mammal March Madness, you fill out a bracket and then follow along on Twitter or on the Mammals Suck … Milk! blog. The first game is on Monday, March 9, and direct elimination games continue until the championship on March 26.
I’ll note that the 2014 winner was Hyena who defeated Orca in the championship game. This year, we’ve got some Serengeti representation as well. But with Lion, Baboon, and Vervet monkey ranked just 8th, 12th, and 13th in the ‘Sexy Beasts’ division, they’re going to need all the cheering-on they can get.
So head on over, print out a bracket, and tell me who you think will make it all the way to the top this year.
(And just to be clear, I am not involved in Mammal March Madness in any way except as a participant. But it looks fun!)
What does the fox say?
By now, you have probably heard of this silly (but hilarious) video that’s been making the rounds of the interwebs lately:
It’s pretty catchy, not just because it’s ridiculous, but because it’s a pretty good question. I mean, how many of you out there have actually ever heard a fox?
The sounds of the bush are one of the many, many things I miss being back here in civilization. From my slightly sketchy corner of Saint Paul, I hear fire crackers and unmuffled engines roaring. Occasionally I get chattered at by an angry squirrel in the back yard. But that’s about it. Nothing like the otherworldly chorus of the Serengeti savanna that Lucy so beautifully described.
The sounds really are incredible and often unbelievable, and I thought I’d share some of them with you. I couldn’t actually figure out how to upload audio files, so I scoured Youtube for the best audio clips I could find and embedded them as videos here.
Zebras: Nothing like horses, these stripy equids sound something like a braying donkey crossed with a barking dog.
Wildebeest: I believe that somewhere in the annals of Zooniverse blogs, there is an audio or video clip of me doing a wildebeest impression. This is better.
Hyenas: Despite being hell-bent on devouring all of my camera traps, these guys are pretty cool. They have a rather large repertoire of very…unusual…vocalizations that are used to communicate in a number of situations. The whoop, which you hear at 0:05 and 0:55, is a long-distance call often used to rally scattered clan members. The laugh at 2:33 is a sign of nervousness or submission. Similar to human voices, hyena vocalizations are individually recognizable to clan-mates. To learn more about hyena vocalizations, check out this blog by hyena expert and director of Masai Mara’s long-term hyena project, Kay Holekamp.
Lions: And finally, for the best, non-hollywood lion roar, scroll about halfway down through our lion research center’s page. This is what they really sound like.
I’ll take any of these noises over the sounds of the city any day.
Lions, cheetahs, and dogs, oh my! (Continued)
Last month, I wrote about how, despite lions killing cheetah cubs left and right, they don’t seem to be suppressing cheetah population size like they do for wild dogs. And, that despite all this killing, that cheetahs don’t seem to be avoiding lions – but I didn’t have radio-collar data for wild dogs.
Well, now I do!
Although we’ve had collared lions continuously since 1984, Serengeti cheetahs and wild dogs were only collared from 1985-1990. We worked with Tim Caro, former director of the cheetah project, to access the historic cheetah data a year ago, but it was only a month ago that we finally tracked down the historic wild dog data. Thanks to a tip by a former Frankfurt Zoological Society employee, we found the data tucked away in the recesses of one of their Serengeti-based storage containers – and Craig braved a swarm of very angry bees to retrieve it!
The good news is that the data was totally worth it. Just like we suspected, even though cheetahs didn’t seem to be avoiding lions, wild dogs were. This map shows lion densities in the background, with cheetah (in brown dots) and wild dog (black triangles) locations overlaid on the lion densities.
It’s a pretty cool contrast. Even though lions kill cheetah cubs left and right, cheetahs do not avoid lions, nor do their populations decline as lions increase. In sharp contrast, wild dogs do avoid lions, and their populations also drop as lions increase. Now, that’s not to say that there weren’t other factors influencing the decline of wild dogs in Serengeti, but across Africa, this pattern seems to hold.
Speaking of wild dogs, has any one seen any in Season 6?
Some Results from Season 4
I was asked in the comments to last week’s blog post if I could provide some feedback about the results of Season 4. If you felt like you were seeing a lot of “nothing here” images, you’re right: of the 158,098 unique capture events we showed you, 70% were classified as having no animals in them. That left 47,320 with animals in them to classify, and the vast majority of these (94%) contained just one species. Here’s the breakdown of what was in all those images:
Maybe it won’t surprise you that Season 4 covered 2012’s wet season, when over a million wildebeest, zebra, and Thomson’s gazelle migrate through our study area. I find it interesting that hartebeest are also pretty numerous, but I wonder if it’s because of that one hartebeest that stood in front of the camera for hours on end.
This pie chart is based on the number of what we call “capture events,” which is the set of 1 or 3 pictures you see every time you make a classification. Once a camera has taken a set of pictures, we delay it from triggering again for about a minute. That way we don’t fill up the camera’s memory card with too many repeats of the same animals before we have a chance to replace them. But a minute isn’t a very long time for an animal that has decided to camp out in front of a camera, and so we frequently get sequences of many capture events that are all of the same animal. One of the things we’ll have to do in turning your classifications into valid research results is to figure out how to find these sequences in the data automatically.
Here’s a sequence of an elephant family hanging out around our camera for the night about a year ago. (Hat tip to dms246 who put together a collection of most of these images to answer the concerned question of some classifiers who saw just one image out of the whole sequence: is that elephant dead or just sleeping?)
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
If you’re interested in how I made the above pie chart, keep reading. But we’re going to get technical here, so if algorithms don’t interest you, feel free to stop.