Winter vacation
In the winter months (northern hemisphere winter, that is), we catch white storks on camera. They’re taking their winter vacation in the Serengeti — and across eastern and southern Africa.
White storks are carnivorous, eating insects, worms, reptiles, and small mammals. A flock of them like this makes me wonder about the diversity of small critters that they eat that we don’t catch on camera. Because they eat small animals, they can sometimes be seen near fires, ready to gobble up those creatures trying to escape flames and smoke.
The white stork has a favorable reputation with people in both Africa and in Europe, because it feeds on crop pests. In the spring, storks leave their wintering grounds and head north to Europe to breed. They build large nests out of sticks and are happy to do so on buildings and other structures with wide, unencumbered supports. And because they are considered useful — and sometime good luck — people allow them to build their nests on buildings. These nests are then frequently re-used year after year.
Several years ago I went to Poland to find my grandmother’s childhood home. This was made a bit challenging because when my grandmother was a child, the area was part of the Austro-Hungarian Empire and the names of everything — towns, streets — were in German. These days, of course, all the names are in Polish. After finding a list of place name translations, I set out to see if I could locate some buildings my grandmother described in her memoirs in a small town in the countryside outside of what is now Wrocław and was then Breslau. One of these was “Grandfather’s [my great-great-grandfather’s] water mill with its stork nest on the roof.” Sure enough, I found a large old building in the middle of town right by the stream. It no longer sported a water wheel, but there on the roof: a stork’s nest, complete with stork.
It’s no Serengeti, but…
Last year, my mom visited me in the Serengeti. We explored the jungle-like Manyara national park, held our breaths as elephants sauntered within reach of the Land Rover, and woke up at 3am to lions roaring next to our campsite in the middle of the Serengeti plains.
This week, I’m visiting my mom in her own little piece of North American grassland. I made a brief escape from the oncoming Minnesota winter to the normally balmy state of Virginia (it’s getting surprisingly cold at night here!) to help my mom with the little piece of paradise she recently purchased. This past spring she sold her home in the DC ‘burbs and moved out to the countryside, somewhere in between fancy horse country and cattle farms. It’s kind of perfect.
Indian grass, broomsedge bluestem and little bluestem, with autumn olive encroaching in the distance.
It might not be as otherworldly as the Serengeti, and there might not be any giraffes browsing by our deck, but my mom is working hard to maintain a piece of native mid-atlantic grassland on her property. Walking the meadow with the state’s conservation officer, we admired at the Indian grass and bluestem and scowled at the thick carpet of green fescue that made the yard inhabitable for the quail we hoped would recolonize. Grassland restoration is currently a major conservation initiative across the United States. Across the country, most native grasslands have been converted for agriculture; the suppression of natural fires has further allowed trees to grow up in meadows and shade out the sun-hungry grass. Ground nesting prairie birds (such as our bobwhite quail) tend to be the biggest losers in this game, because they need just the right amount of cover to be able to thrive. Fescue grass is too thick for baby quail to waddle through; the relentless olive trees grow fast and thick and threaten to turn our meadow into woods. I had no idea that maintaining native prairie was such a battle.
Spending so much time out in the east African bush, I sometimes forget how amazing our own backyards can be. My mom now has foxes, coyotes, bobcats, and black bears, and a fat, happy family of 8 baby wild turkeys that wobbly by at sunset. As much as I miss the Serengeti, the wildlands here are magical in their own way, and I suspect when I leave, I will feel a little homesick.
Pangolin!
A few weeks ago, Snapshot Serengeti volunteers spotted a Pangolin in Season 6. This is the best pangolin shot we’ve ever seen in this project.
Pangolins are rare and nocturnal, so you don’t see them often out in the field. The pangolin species we have in the Serengeti is called the ground pangolin (Manis temmincki), and it ranges from East Africa though much of Southern Africa.
I once went to Kruger National Park in South Africa for a conference and went on a guided tour in my free time; the tour leader asked what we wanted to see, and I shouted out “pangolin!” The tour leader gave me a withering look and we then went out to see the elephants and giraffes and buffalo that the other tourists were eager to see. I really did want to see a pangolin, though. I’ve never seen one in real life.
Pangolins have scales all along their back and curl up into balls like pillbugs when they feel threatened. They hang out in burrows that they either dig themselves or appropriate from other animals. And they have super long tongues that they use to get to ants and termites, their primary food. Pangolins have one baby at a time, and young pangolins travel by clinging to the base of their mother’s tail.
Pangolins don’t have any close living relatives. In fact, they have an order all to themselves (Pholidota). Because of how they look, scientists used to think they were most closely related to anteaters and armadillos. But now with genetic tools they’ve discovered that pangolins are more closely related to the order Carnivora, which includes all cats and dogs. It’s a bit strange to think that pangolins, which are sometimes called “scaly anteaters” have more in common genetically with lions than with actual anteaters, but that’s what the science tells us.
Many thanks to all of you who marked the new pangolin image in the Talk forum. That lets us make sure it gets classified correctly. ‘Pangolin’ was just one of those rare animals that didn’t make it onto the list of animals you can choose from, so our algorithm will classify it as something else, which we will fix by hand.
Who knew?
Just wanted to brighten your morning with a pretty unbelievable video that has nothing to with the Serengeti. Frogs freeze. That’s right. They don’t hibernate, they freeze. I couldn’t embed this video from NOVA’s Science Now site, but just click here to watch!
I hope that rocks your morning as much as it rocked mine.
Closer look: civets and genets
Civets
The African civet Civettictis civetta is the sole terrestrial civet found in Africa, the rest being found in Indian subcontinent. It is a heavy set cat-like animal and is still referred to as a civet-cat by some though it is not a member of the felids. Civets have a white body with black blotchy spots. They have a black face mask and black legs; the tail appears ringed with a thick black line running down the top. They have an erectile dorsal crest which they raise when alarmed or in aggression. This can be seen on a few of our camera-trap images.
The African civet is most famous for its musk that is used in the perfume trade. Don’t get the wrong impression, it smells terrible, but helps fix scent. Its use has mostly been replaced with synthetic fixers these days which is good news for civets. Civet farms are not regulated and animals are usually kept in small cages from which they are ‘milked’ daily. The real use of their perineal gland which is situated near the anus is to paste an object such as a tree to act as territorial sign post.
African civets are omnivorous, eating a wide range of vertebrate and invertebrate prey as well as taking advantage of fallen fruit. They are clumsy killers and often employ a bite and retreat or bite and throw tactic, where prey is bitten and thrown before quickly running away. The prey is hopefully immobilised so the civet can return to inflict the killing bite. Scent and sound are the predominant senses used by civets. They are classified as Least Concern on the IUNC Red List (International Union for Conservation of Nature)
Genets
The Genet family has 15 subspecies in Africa and these are all still hotly debated. The Serengeti is home to at least two of these, the Common Genet Genetta genetta and the Rusty Spotted or Central African Large Spotted Genet Genetta maculate. It is very hard to tell them apart, especially in a fleeting camera-trap picture but the Common Genet usually has a white tipped tail and the Rusty Spotted Genet has a black tipped tail.
They are small agile mammals that resemble a cat with short legs. Their silvery grey coat is marked with black spots in the Common Genet and black to brown spots in the Rusty Spotted Genet. They have dark marks either side of the muzzle below the eyes giving them a slightly racoon-like look. The tail which is banded is almost as long as the body and can appear quite bushy when alarmed.
The Genets are mainly carnivorous and they will eat mammals, birds, insects and reptiles. They hunt in trees and on the ground and are extremely dextrous. They spend their days in holes in trees, thick bushes, rocky crevices and sometimes in ground holes. Like civets they also have a perineal gland that they use for scent marking. To do this they will stand on their forefeet in a handstand posture and rub the raised gland on a tree or bush. Both Common Genet and Rusty Spotted Genet are classed as Least Concern on the IUNC Red List.
Summary of the Experts
Last week, william garner asked me in the comments to my post ‘Better with experience’ how well the experts did on the about 4,000 images that I’ve been using as the expert-identified data set. How do we know that those expert-identifications are correct?
Here’s how I put together that expert data set. I asked a set of experts to classify images on snapshotserengeti.org — just like you do — but I asked them to keep track of how many they had done and any that they found particularly difficult. When I had reports back that we had 4,000 done, I told them that they could stop. Since the experts were reporting back at different times, we actually ended up doing more than 4,000. In fact, we’d done 4,149 sets of images (captures), and we had 4,428 total classifications of those 4,149 captures. This is because some experts got the same capture.
Once I had those expert classifications, I compared them with the majority algorithm. (I hadn’t yet figured out the plurality algorithm.) Then I marked (1) those captures where experts and the algorithm disagreed, and (2) those captures that experts had said were particularly tricky. For these marked captures, I went through to catch any obvious blunders. For example, in one expert-classified capture, the expert classified the otherBirds in the images, but forgot to classify the giraffe the birds were on! The rest of these marked images I sent to Ali to look at. I didn’t tell her what the expert had marked or what the algorithm said. I just asked her to give me a new classification. If Ali’s classification matched with either the algorithm or the expert, I set hers as the official classification. If it didn’t, then she, and Craig, and I examined the capture further together — there were very few of these.
Huh? What giraffe? Where?
And that is how I came up with the expert data set. I went back this week to tally how the experts did on their first attempt versus the final expert data set. Out of the 4,428 classifications, 30 were marked as ‘impossible’ by Ali, 1 was the duiker (which the experts couldn’t get right by using the website), and 101 mistakes were made. That makes for a 97.7% rate of success for the experts. (If you look at last week’s graph, you can see that some of you qualify as experts too!)
Okay, and what did the experts get wrong? About 30% of the mistakes were what I call wildebeest-zebra errors. That is, there are wildebeest and zebra, but someone just marks the wildebeest. Or there are only zebra, and someone marks both wildebeest and zebra. Many of the wildebeest and zebra herd pictures are plain difficult to figure out, especially if animals are in the distance. Another 10% of the mistakes were otherBird errors — either someone marked an otherBird when there wasn’t really one there, or (more commonly) forgot to note an otherBird. About 10% of the time, experts listed an extra animal that wasn’t there. And another 10% of the time, they missed an animal that was there. Some of these were obvious blunders, like missing a giraffe or eland; other times it was more subtle, like a bird or rodent hidden in the grass.
The other 40% of the time were mis-identifications of the species. I didn’t find any obvious patterns to where the mistakes were; here are the species that were mis-identified:
| Species | Mistakes | Mistaken for |
| buffalo | 6 | wildebeest |
| wildebeest | 6 | buffalo, hartebeest, elephant, lionFemale |
| hartebeest | 5 | gazelleThomsons, impala, topi, lionFemale |
| impala | 5 | gazelleThomsons, gazelleGrants |
| gazelleGrants | 4 | impala, gazelleThomsons, hartebeest |
| reedbuck | 3 | dikDik, gazelleThomsons, impala |
| topi | 3 | hartebeest, wildebeest |
| gazelleThomsons | 2 | gazelleGrants |
| cheetah | 1 | hyenaSpotted |
| elephant | 1 | buffalo |
| hare | 1 | rodents |
| jackal | 1 | aardwolf |
| koriBustard | 1 | otherBird |
| otherBird | 1 | wildebeest |
| vervetMonkey | 1 | guineaFowl |
Night of the Lion
Most of you have probably seen this picture:
Kill in action
As well as the ones after it:
Joined by a pridemate.
staring out of view of the camera…
Three minutes later…
Trade-off again…
Where are the leftovers?
This series of photos was taken at site H11 along the Loyangalani river and remains, to me, one of the most amazing accomplishments of our camera trap survey to date.
First, seeing a kill is rare. In the 47 years that the Lion Project has been watching Serengeti’s lions, we’ve only seen lions with about 4,000 carcasses; of those, we’ve only actually seen them in the act of killing 1,100 animals. That might sound like a lot, but with one or two people on the ground, almost every day of the year, racking up nearly 50,000 sightings, that’s not that often.
I don’t love this series simply because this random, stationary, complacently-stuck-to-a-tree camera trap caught this rather rare event – but because it goes on to document the story that follows: A single lioness takes down a zebra much bigger than herself. Within minutes, her sister joins her (free meal!). Note how big their bellies already are though, when they begin to eat. These aren’t particularly hungry lions to begin with. About 45 minutes later, they are staring out of view of the camera, and then comes a group of hyenas. The carcass goes back and forth between them throughout the night, with a jackal darting in to sneak a nibble.
Food stealing, or kleptoparasitism, is a major part of life for Serengeti carnivores. Contrary to long-standing popular belief (reinforced by the Lion King), hyenas are not skulking scavengers living only off others’ leftovers. Hyenas are quite adept predators and scavenge only about 40% of their diet; lions scavenge at least 30% of theirs. And, in fact, lions steal a lot more food from hyenas than is apparent at first glance. More often than not, when we see hyenas lurking anxiously around a pride of lions demolishing a carcass, it’s because hyenas made the kill, and lions stole it away. Research from Kenya suggests lions might actually suppress hyena populations simply by stealing their food.
On the flip side, work from Botswana suggests that hyenas are able to steal food from lions if and only if hyenas outnumber lions by at least 4 to one, and there are no adult male lions present. (Remember, males are half again as big as females: hyenas don’t stand a chance.) But observations that Craig and a former graduate student made from the Ngorongoro Crater further revealed that even when lions do give up a kill, they are so full they can barely move – it’s simply not worth the effort to fend off hyenas any more.
So, kleptoparasitism is a part of life if you are a Serengeti carnivore, but it’s not always as simple as the movies make it out to be. It’s a pretty cool mechanism that might be driving predator dynamics though – I just wish it weren’t so hard to test!!
The lost lions of the Transect Steady pride
### Today we’ve got a guest post by our very own Daniel Rosengren, lion tracker (& photographer) extraordinaire. ###
It started with some mysterious footprints around the Loliondo Kopjes. There were a lot of fresh paw marks in the mud following the road. I could tell it was a big pride but the only big pride with a territory nearby was the Young Transects. But I could not hear their collar. Neither could I hear any of our other prides. I drove around for a while looking for lions, especially on the rocks and under trees. I didn’t find any and guessed it could have been the Young Transect lions anyway, only without the collared female.
A couple of weeks later I was headed out east when I soon caught eye on a big group of lions. As I drove closer I realized they weren’t any lions I knew. I tried to get photos of all of them but it wasn’t easy knowing who you’d already got in a group of 17 lions. Luckily they all started walking along the track. All I had to do was park ahead of them and take photos as they passed one by one. Once I had photos of all their left sides I went home to try to figure out who they were.
I concentrated on the older females as the youngster probably never had been seen before by the Lion Project. After a while I found a couple of matches. It was TR86 and TSF from the Transect Steady pride, not seen since December 2009, almost three and a half years earlier. But the last time they were seen regularly in our study area was in 2008.
Now I contacted TANAPA and the vets to organize a collaring of one of the females. They were coming. I drove back to the place where I’d seen the lions and hoped they hadn’t walked too far. I found them in the shade of a tree. Then a long wait started for the vets to organize themselves and drive all the way from Fort Ikoma. Once they came, the collaring went smoothly, the rest of the pride watching from a distance.
All of the lions of the TS pride, watching us warily as we try and create enough space to safely collar our lion.
Daniel fitting the collar.
TANAPA vet preparing to take samples.
It’s pretty exciting to to be this close to a lion. Their paws are BIG.
About a week later I found the pride just outside the northern edge of our study area along the Pipeline track. Two more old females known since before had joined them, TR93 and TR106. Then they disappeared. So two weeks later I decided to search for them and drove along the Pipeline track north. But instead of driving on the actual track, which in many places was disappearing because of little use, I drove parallel to it, hitting all hilltops to be able to pick up the radio signal from a greater distance. The drive was terrible as the hills in the area are specked with large rocks and I had to drive dead slow. I held on the the steering wheel as little as possible. Having no power steering means that every time I hit a rock I risk breaking thumbs or worse.
I picked up the signal after a while but I still had to pass several hills before finally finding them, right by the track at a river confluence. That was quite far north of our study area and too far to go and see them on a weekly basis. The future will have to show where they finally settle.
Better with experience
Does experience help with identifying Snapshot Serengeti images? I’ve started an analysis to find out.
I’m using the set of about 4,000 expert-classified images for this analysis. I’ve selected all the classifications that were done by logged-in volunteers on the images that had just one species in them. (It’s easier to work with images with just one species.) And I’ve thrown out all the images that experts said were “impossible.” That leaves me with 68,535 classifications for 4,084 images done by 5,096 different logged-in volunteers.
I’ve counted the number of total classifications each volunteer has done and given them a score based on those classifications. And then I’ve averaged the scores for each group of volunteers who did the same number of classifications. And here are the results:
Here we have the number of classifications done on the bottom. Note that the scale is a log scale, which means that higher numbers get grouped closer together. We do this so we can more easily look at all the data on one graph. Also, we expect someone to improve more quickly with each additional classification at lower numbers of classifications.
On the left, we have the average score for each group of volunteers who did that many classifications. So, for example, the group of people who did just one classification in our set had an average score of 78.4% (black square on the graph). The group of people who did two classifications had an average score of 78.5%, and the group of people who did three classifications had an average score of 81.6%.
Overall, the five thousand volunteers got an average score of 88.6% correct (orange dotted line). Not bad, but it’s worth noting that it’s quite a bit lower than the 96.6% that we get if we pool individuals’ answers together with the plurality algorithm.
And we see that, indeed, volunteers who did more classifications tended to get a higher percentage of them correct (blue line). But there’s quite a lot of individual variation. You can see that despite doing 512 classifications in our set, one user had a score of only 81.4% (purple circle). This is a similar rate of success as you might expect for someone doing just 4 classifications! Similarly, it wasn’t the most prolific volunteer who scored the best; instead, the volunteer who did just 96 classifications got 95 correct, for a score of 99.0% (blue circle).
We have to be careful, though, because this set of images was drawn randomly from Season 4, and someone who has just one classification in our set could have already classified hundreds of images before this one. Counting the number of classifications done before the ones in this set will be my task for next time. Then I’ll be able to give a better sense of how the total number of classifications done on Snapshot Serengeti is related to how correct volunteers are. And that will give us a sense of whether people learn to identify animals better as they go along.
This is what grant applications do
I’ve been working on a federal grant application the last couple of weeks. It’s left me feeling a bit like this:
The grant was originally due this upcoming Thursday, but with the government shutdown showing no signs of ending, who knows what will happen? The National Science Foundation’s website is unavailable during the furlough, meaning that nobody can submit applications. So we’ve all been granted an unexpected extension, but we’re not sure until when.
The grant I’m applying for is called the Doctoral Dissertation Improvement Grant. It’s an opportunity for Ph.D. students to acquire funding to add on a piece to their dissertation that they wouldn’t otherwise be able to do. I’m applying for funds to go down to South Africa and work with a couple of folks from the conservation organization Panthera to collate data from two sites with long-term carnivore research projects. Their research team currently has camera surveys laid out in two reserves in Kwazulu-Natal, South Africa: Phinda Private Game Reserve and Mkhuze Game reserve. Now, the cool thing about these reserves is that they are small, fenced, and pretty much identical to each other…except that lions have been deliberately excluded from Mkhuze.
Now, one of the biggest frustrations of working with large carnivores is that I can’t experimentally isolate the processes I’m studying. If I want to know how lions affect the ranging patterns and demography of hyenas, well, I should take out all the lions from a system and see what happens to the hyenas. For obvious reasons, this is never going to happen. But the set-up in Phinda and Mkhuze is the next best thing: by holding everything else constant – habitat, prey – I can actually assess the effect of lions on the ranging and dynamics of hyenas, cheetahs, and leopards by comparing the two reserves.
Camera surveys (yellow dot = camera) in Serengeti (left) and Phinda/Mkhuze (right).
So, that’s what I’m working on non-stop until whenever it turns out to be due. Because this would be a really cool grant to get. I’m currently working on analyzing some of the camera trap data from Seasons 1-4 and hope to share some of the results with you next week. Until then, I’m going to continue to be a bit of a zombie.
Snapshot Serengeti 