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!
Snapshot Serengeti’s first scientific publication — today!
Champagne corks will be popping tonight. Snapshot Serengeti’s first peer reviewed scientific publication comes out today in Nature’s Scientific Data journal. Please give yourselves a round of applause, because we’d never have been able to do this without you.
The paper is a “data descriptor” instead of a traditional research article, meaning that we describe the detailed methods that led to the Snapshot Serengeti consensus dataset. In addition to describing all the excrutiating details of how we set the cameras in the field, we talk about the design of Snapshot Serengeti, setting retirement rules and aggregation algorithms to combine all of our answers into a single expert-quality dataset. We don’t talk about the cool ecological results just yet (those are still only published in my dissertation), but we do talk about all the cool things we hope the dataset will lead to. The dataset is publicly available here. Anyone can use it — to ask ecological questions about Serengeti species, evaluate better aggregation algorithms for citizen science research, or — we get this a lot — use the images plus consensus data to train and test better computer recognition algorithms.
Feel free to download the dataset and explore the data on your own. We’d love to hear what you find!