Every once in a while, a camera gets knocked off a tree and ends up pointing up into the tree where there are many grassy balls hanging from the branches. We have one of these cameras in Season 5, and it is taking pictures like this one:
What are those odd grassy balls? Why, they’re the nests of weaver birds. My Birds of East Africa book lists a dozen species of weavers in the Serengeti, and most of them have a yellow and black pattern. Here’s what some of these guys look like close up.
Several years ago, I watched through a Lion House window as a weaver bird build its nest from scratch. The bird started with just a branch, one with something of a knot at the end where a twig may have split off in the past. The weaver grabbed a long blade of grass and wrapped it around that knobby joint and tucked the blade under itself, as you might do if you were tying your shoe. Then it got another blade of grass and wove that through the loop it had created with the first blade, tucking it securely back under and through the loop a second time. It continued to add blades for the next twenty minutes or so, such that the grass formed two clumps, one sticking out of either side of the knot.
(Aside: the soundtrack is completely coincidental; field assistant John was cooking something in the kitchen while listening to music.)
Straddling the two clumps, with one talon hanging on to each, the weaver then took a long blade from one clump and wove its end back up into the other clump. The result was a loop. The bird pulled additional grass from one clump to the other and strengthened the loop. Bit by bit.
I watched for over a half-hour, but I had work to do, too. So I left the little weaver to its task, and checked in again that evening before the sun set. There it was, a hefty wreath of grass hanging from the end of a tree branch.
I checked again a couple days later. The weaver had been working on filling in grass around the sides to form the ball shape.
Three days later the ball shape was becoming apparent (and I finally decided to take pictures outdoors instead of through the window, so that they’re better in focus).
Aha! I caught a decent shot of the builder. My bird appears to be a Vitelline Masked Weaver male. (Although, my book also says that the top of the head ought to be rather chestnut color and this guy has maybe only a little bit of chestnut and rather brown markings on the back instead of black. Maybe it’s a young male?) These guys generally are found solitary or in pairs, which explains why I saw just one of them building a nest in a tree all alone. And their nests are “distinctive onion-shaped nests with an entrance hole at the bottom.” Looking good…
Five days later Mr. Vitelline’s work was looking very much like a nest.
Five days later was also my last day in the Serengeti, so I didn’t see further developments of this nest. But I suspect it was completed and became a comfortable abode for its industrious builder.
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.