You’ve undoubtedly seen it: Grass. Tall waving grass. Lots of it. From here to the horizon. If you’re itching to get images of animals to classify, the “nothing here” grass images can seem annoying. Some people find the grass images soothing. The animals themselves, well, a lot of them seem to like it.
Some animals find that tall grass is nice for concealing themselves from predators, like these guys:
Or this impala:
And some animals think the grass is nice for eating, like here:
This post is brought to you by Faulty Cameras that switch unexpectedly to video mode when they’re not supposed to. These Season 5 videos have no sound, but capture some of the movement you don’t get with the photographs, so I thought you might like them.
Stuck. Part 2.
##### Today’s post is a continuation of last week’s adventure, written by Patrik Dousa. #####
When we left off the story from last week, all of us in the Serengeti team were out deep in the sour tern range of the Serengeti trying to free a land rover from thick mud. All we accomplished was securing the range rover even deeper in the mud. From 1/4 of the wheel being submerged to a half, with the bumper touching the ground. Good going. A beautiful sunset was going to occur in an hour or so and the last place to be at that point was in the middle of a hazardous plain with a large pride of lions waking up for their nighttime prowls.
The lions are still watching us from their mesas to the north and Ali is figuring out the next move. I thought it was a clear decision. Leave. Now. Have I described to you the fortitude and diligence of a lion researcher? A job that requires you to spend most of your time in the dry plains with the only the basic minimum requirements to sustain you doesn’t attract individuals who give up too easily. No, Ali and George see the sunken rover as a challenge that must be faced. We aren’t leaving, not without a fight.
Just then a tourist vehicle pulls up along a road about a half-mile from our area on the other side of the uncrossable mud plains. The guide is in the process taking them back home to one of the southern lodges and apparently decided to stop, having spied the magnificent example of male lion that was observing our vehicle. The new arrival attracted King Simba’s attention and the powerful elegant beast starts walking towards the tourists. I can see their excitement mount through my binoculars — this moment is going to be the highlight of their trip. George and Ali are laboring through shovelfuls of the thickest, reddest, peatiest mud you can imagine and only a short distance away, well-scrubbed observers are preparing themselves for the the apex of their Serengeti experience. Such is life.
I see a bold cub follow his master lion and play around his feet incurring his wrath for a moment. The king playfully swats back and raises his head to the heavens letting out an immense roar to the delight of the tourists. The greatest show on earth — with our little car-trouble side-show of going on right in the background. “Who are those crazy people back there?” they must have asked their guide. “Well, they’re professionals, so they must know what their doing.” the guide is certain to have responded.
The lion’s roar triggered a slow migration of the lionesses and their cubs from the low mesas to the area closer to the tourist vehicle where the male lion had settled. As the single file procession began, we felt a wave of relief since the pride was now headed away from our rover. A few more attempts to drag out the stuck vehicle failed. By now the sun is steadily growing larger and more rosy as it begins its decent. The sky eventually reaches the particular hue that Ali reads as our signal to leave.
We secured the vehicle and took all the valuables and began a slow retreat back thinking, “please don’t get stuck” on repeat until we got back on the main road. The pink sun blossomed into a deep red bloom that backlit the acacia tree line creating the beautiful silhouetted postcard image that the Serengeti is so well know for. The mood in the car was impervious to these romantic supplications. Exhausted and temporarily defeated, the crew made the long journey back toward the research house.
Being the visitor who expended the least amount of sweat that day, I suggested that we stop at the local canteen Seronera and that I’d treat everyone to a chicken and rice dinner and a Stoney Tangawizi (the extra spicy ginger ale that is everybody’s favorite drink in Tanzania). This turned out to be a very cost effective way to turn the sour mood sweet — just a few bucks per plate and brew to get everyone back to their happy place. Soon the team was back to the bantering with the locals and planning tomorrow’s adventure. That was my last night at the Serengeti, the next day I was back on the road to Arusha. Ali messaged me later and mentioned that they were able to round up a crew to go back and successfully drag out the rover the next day. This did not surprise me since I had well learned: you can’t keep a lion research team down for long.
The trouble with shade
Who knew that shade could be so problematic? A couple of weeks ago, I wrote about how shade seems to be my biggest obstacle in reconciling how the cameras see the world vs. what is actually going on. My job is to figure out how to make things right.
To start with, the camera traps are up on trees. Mostly. As you know, the cameras are on a rough grid layout – 225 grid cells, each 5km2 (2.236km on each side) — covering a total of 1,125 km2 of Serengeti’s center. This kind of design makes sure that we are covering enough of the landscape to capture the bigger picture of animal distributions and movements. Each camera is roughly at the center of each grid cell – on the closes suitable tree to that center point. Some trees are big and shady; some are small and spindly. In the woodlands, there are trees everywhere; on the plains, the camera-trap tree can be the only tree for miles. And sometimes there are no trees at all, and here the cameras get put up on metal poles.
These different habitats are important to capture. I think that animals might behave very differently in areas with lots of trees than they do in areas with very few trees. When it comes to the aggressive interactions between carnivores, for example, trees, shrubs, and tall grass provide great hiding places for the smaller species. It’s like trying to hide from someone you don’t like in an empty room vs. in a really huge, crowded shopping mall.
The problem is that camera traps work better in some habitats than others – at least for certain species. Say you are a huge, muscle-bound lion. Even standing is tiring in the Serengeti heat, and you spend your days breathing heavily even at rest. You like shade. A lot. If you are out in the open plains, a single shade tree will stick out for miles, and you’ll probably work your way to it. Chances are, that tree has a camera. In the woodlands, though, there are lots of trees. And the camera trap could be on any one of them. So even if you’re searching for shade, the chances of you walking past the camera trap in the woodland are far smaller – just because there are so many trees to choose from.
Here’s a map of the study area – green shows more densely wooded areas, whereas yellow marks the plains. Camera traps that have captured lions are shown with circles; the bigger the circle, the more lions were seen at that trap. I know for a fact that there are more lions in the northern half of that map than in the southern half, but the lions out on the plains seem to really like getting their picture taken!
The pattern looks a little better at night than in the day, but it’s not perfect. So perhaps shade isn’t the only thing affecting how these cameras “see” lions in different habitats.
As depressing as this problem seems at first glance, I’m optimistic that we can solve it (enter Kibumbu’s new GPS collar!), but those methods are material for another day. In the meanwhile, what else do you think might be going on that attracts lions, or other animals to trees, besides shade?
Stuck. Part 1.
Stuck. Surrounded by lions. Please come.
This is not a text message that you’d necessarily expect to get on your cell phone…unless you work as a lion researcher in the Serengeti like Ali does. Receiving this text in the early afternoon, she takes the news in stride as a necessary task that needed to be finished before dark. I on the other hand, as a visitor, am charged up and nonplussed with the drama of it all. George, one of the field research assistants on a lion tracking expedition, obviously needs help and pronto, so we are on our way out within a few minutes. In the wild Serengeti, a few minutes can separate success from tragedy — the research team has an exceptional awareness of this and also the discipline to do what it needs to be done in a methodical and prompt manner as Ali is demonstrating to me at this moment.
We track George and his land rover down just like we do lions. Each rover is outfitted with the same tracking unit that is on the collar of each radio-tracked lioness. So we chase the rover’s signature signal deep into the southern range, driving on the dirt roads as fast as we can safely afford. As the day draws towards a close, the animals become restless. Elephants trumpet in the distance. A serval — a beautiful African wild cat one doesn’t see everyday– trots across the road and disappears in the brush. Normally such a sighting would warrant an immediate stop, but not today.
Finally, we go as far as the roads can take us and we must venture in the unmarked grassy plains that are a minefield of axle-breaking holes and mud-traps. Driving off road is risky business in the daytime –as George was just reminded of — and completely a fool’s errand in the nighttime. Ali looks for the tell-tale signs in grass patch coloration that indicate a possible hole as she swerves deftly through the treacherous terrain in a labored crawl.
Finally on the horizon, we sight George and his rover axle deep in a seemingly stable area. The dry cracked surface, however, masks a vast mud hole created by the recent rains. This is the worst kind of environmental trap that even a seasoned veteran like George can fall prey too. With a lighthearted smile that belies any frustration, George explains how he tracked a pride of lions into this area and was surprised by the sudden drop into the mud. Luckily, our rover remains in the solid area just short of George’s rover. We check the area and see that the lions have moved off to a series of small mesas to the north. It’s safe enough to exit the vehicles as long as one of us keeps a 360 degree lookout.
Our cellphones at that point record no bars, so as Ali readies a tow line, she inquires how George was able to get a message out.
The calm exterior and wry banter of every lion researcher I’ve met is always the counterpoint to the fierce passion and iron discipline at their core. George is all smiles and laughs a bit as he recounts the sinking feeling he had when he saw that he had no bars on his cellphone and lions surrounding three sides of the vehicle. A thickly maned adult male lion stood watch right outside the drivers side as if he sensed George’s desperation.
A good scientist, when faced with a problem, puts together an experiment to test its boundaries. Perhaps the cell phone could be made to transmit somehow? As George raised his hand up and out of the vehicle he noticed to a single bar flicker on and off. This observation made him hatch a plan that he reflected on as he eyed the attentive dark-maned sentinel waiting outside along with the multiple groups of lionesses and cubs surrounding him.
The day was not going to get any longer so George, did exactly what he contemplated: he composed his terse message on his phone, climbed out the window onto the roof rack, and jumped up several times pressing the send key until the signal caught and the phone indicated the message was sent. Then he waited for the animals realize that he was still out of their range and relax back down to their lazy poses and before slipping back into the car to await rescue.
By the end of the story, the tow cable is fastened and mud traction ladders are in position under the rear wheels of the rover. Ali is ready to begin the first effort to pull the car. The gears lock in, the engine strains, the wheels spin, and…Georges car slips off of the ladders and deeper into the mud.
To be continued…
Conference on Science Communication
Last week I attended a conference on science communication in Cambridge, Massachusetts. It was an intense few days, but totally worthwhile and interesting. There were fifty of us grad students, seven 3-person panels of various experts, and more food than you can possibly imagine. (The sheer quantity of food rivaled that put out by Zooniverse for its workshops — and that’s saying something.) The grad students spanned all sorts of science disciplines, but the conference was arranged by astronomers, so there was, I think, a disproportionate number of people there who like to try to figure out what’s going on up in space. I really enjoy talking with researchers in other disciplines because there are rather distinct cultures across the difference sciences. It’s interesting to see what various fields value and how they do things. And frankly, I don’t want to reinvent the wheel and so prefer to borrow best practices from elsewhere rather than figure them out from scratch.
The more I talk to astronomers, the more I think ecologists can borrow stuff from them. I mean, astronomers are pretty constrained in their science. All they can do is observe stuff out there in space and then try to be super clever to figure out what’s going on. Meanwhile, here on earth, we ecologists can do all that sort of observing PLUS we can manipulate the world to do experiments. Because we can do hands-on experiments, that’s a big part of ecology, but as the tools are getting more sophisticated to collect the sort of large-scale observational data that astronomers already have, I think we may be able to learn new things about the living world that are hard to figure out from experiments alone. And we might be able to borrow ideas from astronomers on how to do so.
For example, check this out. It’s a hand-out from one of our panel speakers, Dr. Alyssa Goodman, an astronomer at Harvard, who talked with us about communicating science with other scientists in different disciplines.
So the cool thing that caught my eye was: Zooniverse! (I added the red oval and arrow; the rest is original.) But this whole Seamless Astronomy thing sounds like a neat effort to integrate large amounts of data, visualization, research, and social media into something coherent that people can use to explore and combine some large astronomy data sets. There’s nothing like this (that I am aware of) going on in ecology, but the sorts of things this project figures out in the astronomy world could be useful to us over in ecology.
Of course some things will always be different in different disciplines. One thing we did at this conference was to introduce ourselves and our research in short one-minute “pop talks.” We had to avoid using jargon, which is hard when you’re steeped in your science all day every day. To reinforce the no-jargon rule, everyone was given big, brightly colored sheets of paper — one that read JARGON and one that read AWESOME. If someone used jargon, the audience would all hold up their JARGON flags. If someone explained something well without jargon, up went the AWESOME signs. This sort of feedback worked really well and we all got good at speaking without jargon fairly quickly.
But it was easier for some of us than others. I got to stand up and talk about how I study “plants and animals and how they interact with one another,” which is pretty understandable to anyone. I felt bad for the particle physicists and molecular chemists who had to try to describe their work without using the technical terms for the things they study; but they did well: “The world is made up of little tiny particles. I study how these particles wobble, and in particular how they wobble when you shine really bright lights on them.”
Lucky for us, we get to look at savanna landscapes and amazing animals as we do our research, so I’ll appreciate the perks of ecology as I get back to work now that the conference is over.
Apparently I am no longer invincible. I hear this is what happens when you turn 30 (next week!) but I didn’t believe it. Nonetheless, reality cares not for what I do and don’t believe, and my backcountry vacation in the Yellowstone and Tetons (with bears! and marmots! and moose!) left me with a cold that has knocked me flat on my back.
So, instead of trying to blog, in between bites of chicken & stars soup and through the fog on NyQuil, about why shade skews our perception of where animals are hanging out, I am instead suggesting you read this gorgeous blog post by one of the students with the Masaai Mara Hyena Project. Masaai Mara is part of the larger Serengeti-Mara ecosystem, which spans Kenya and Tanzania. Masaai Mara falls on the Kenyan side of the border, and Serengeti on the Tanzanian side.
The hyena project is overseen by Kay Holekamp’s lab at the University of Michigan. I had the privilege of spending a week or so with these guys in the Mara in 2012, trying to fool their hyenas with our lifesized lion dummies. They are an amazing, fun, and productive group doing really cool research about the intersection of hyena physiology and behavior. It’s sort of the flip side of what I’m interested in – whereas I’m interested in how animal behaviors translate upwards into larger scale dynamics of populations, Holekamp’s group is trying to understand the physiological drivers of, and implications of, these behaviors. For example, hyenas live in incredibly hierarchical societies. What makes a hyena “top dog”, if you will, in a clan? And in turn, how does that dominance status affect that individual’s health? Their reproduction? Why do lower-ranking individuals help higher ranking individuals acquire food, when they don’t actually get to eat it? Stuff like that. It’s pretty cool. So check them out!
We (or at least the lions) miss the migration
Hi everyone —
It’s Friday and we’re short a guest post! Since I’ve just returned from a backcountry holiday in Yellowstone, and Margaret is at a scientific conference, I thought I’d fill this space with a quick video of last year’s migration. I recruited Jason Adams, a Serengeti-based hot-air balloon pilot (and Canada’s reigning hot-air ballooning champion), to help me capture the scene on his Go-Pro.
Cute Baby Elephant
I hope you’ve been having fun with the new Season 5 images. I have. It’s been about a week since we went live with Season 5, and we’re making good progress. It took under two weeks to go through the first three seasons in December. (We had some media attention then and lots of people checking out the site.) It took about three weeks to finish Season 4 in January. According to my super science-y image copy-and-paste method, it may take us about two months to do Season 5:
And that’s fine. But I was curious about who’s working on Season 5. The Talk discussion boards are particularly quiet, with almost no newbie questions. So is everyone working on Season 5 a returnee? Or do we have new folks on board?
I looked at the user data from a data dump done on Sunday. So it includes the first 5 or so days of Season 5. In total, there are 2,000 volunteers who had contributed to 280,000 classifications by Sunday! I was actually quite amazed to see that 6% of the classifications are being done by folks not logged in. Is that because they’re new people trying out the site — or because there are some folks who like to classify without logging in? I can’t tell.
But I can compare Season 5 to Season 4. We had 8,300 logged-in volunteers working on Season 4. Of all the classifications, 9% were done by not-logged-in folks. That suggests we have fewer newcomers so far for Season 5. But then we get to an intriguing statistic: of those 2,000 volunteers working on Season 5 in its first five days, 33% of them did not work on Season 4 at all! And those 33% apparently new folks have contributed 50% of the (logged-in) classifications!
So what’s going on? Maybe we’re getting these new volunteers from other Zooniverse projects that have launched since January. Maybe they’re finding us in other ways. (Have you seen that the site can be displayed in Finnish in addition to Polish now?) But in any case, welcome everyone and I hope you spot your favorite animal.
Me, I found this super cute baby elephant just the other day:
Space and time
If you are a nerd like me, the sheer magnitude of questions that can be addressed with Snapshot Serengeti data is pretty much the coolest thing in the world. Though, admittedly, the jucy lucy is a close second.
The problem with these really cool questions, however, is that they take some rather complicated analyses to answer. And there are a lot of steps along the way. For example, ultimately we hope to understand things like how predator species coexist, how the migration affects resident herbivores, and how complex patterns of predator territoriality coupled with migratory and resident prey drive the stability of the ecosystem… But we first have to be able to turn these snapshots into real information about where different animals are and when they’re there.
That might sound easy. You guys have already done the work of telling us which species are in each picture – and, as Margaret’s data validation analysis shows, you guys are really good at that. So, since we have date, time, and GPS information for each picture, it should be pretty easy to use that, right?
Sort of. On one hand, it’s really easy to create preliminary maps from the raw data. For example, this map shows all the sightings of lions, hyenas, leopards, and cheetahs in the wet and dry seasons. Larger circles mean that more animals were seen there; blank spaces mean that none were.
And it’s pretty easy to map when we’re seeing animals. This graph shows the number of sightings for each hour of the day. On the X-axis, 0 is midnight, 12 is noon, 23 is 11pm.
So we’ve got a good start. But then the question becomes “How well do the cameras reflect actual activity patterns?” And, more importantly, “How do we interpret the camera trap data to understand actual activity patterns?”
For example, take the activity chart above. Let’s look at lions. We know from years and years of watching lions, day and night, that they are a lot more active at night. They hunt, they fight, they play much more at night than during the day. But when we look at this graph, we see a huge number of lion photos taken between hours 10:00 to 12:00. If we didn’t know anything about lions, we might think that lions were really active during that time, when in reality, they’ve simply moved 15 meters over to the nearest tree for shade, and then stayed there. Because we have outside understanding of how these animals move, we’re able to identify sources of bias in the camera trapping data, and account for them so we can get to the answers we’re really looking for.
So far, shade seems to be our biggest obstacle in reconciling how the cameras see the world vs. what is actually going on. I’ve just shown you a bit about how shade affects camera data on when animals are active – next week I’ll talk more about how it affects camera data on where animals are.
On Wednesday, I wrote about how well the simple algorithm I came up with does against the experts. The algorithm looks for species that have more than 50% of the votes in a given capture (i.e. species that have a majority). Commenter Tor suggested that I try looking at which species have the most votes, regardless of whether they cross the 50% mark (i.e. a plurality). It’s a great idea, and easy to implement because any species that has more than 50% of the vote ALSO has the plurality. Which means all I have to do is look at the handful of captures that the majority algorithm had no answer for.
You can see why it might be a good idea in this example. Say that for a particular capture, you had these votes:
You’d have 21 votes total, but the leading candidate, impala, would be just shy of the 11 needed to have a majority. It really does seem like impala is the likely candidate here, but my majority algorithm would come up with “no answer” for this capture.
So I tried out Tor’s plurality algorithm. The good news is that 57% of those “no answers” got the correct answer with the plurality algorithm. So that brings our correct percentage from 95.8% to 96.6%. Not bad! Here’s how that other 3.4% shakes out:
So now we have a few more errors. (About a quarter of the “no answers” were errors when the plurality algorithm was applied.) And we’ve got a new category called “Ties”. When you look for a plurality that isn’t over 50%, there can be ties. And there were. Five of them. And in every case the right answer was one of the two that tied.
And now, because it’s Friday, a few images I’ve stumbled upon so far in Season 5. What will you find?