Here’s a great post by the BBC about some genetic work that has just been done to shed light on the evolutionary history of lions. Apparently, it’s a bit tricky reconstructing lion history due to the fact that they don’t fossilize particularly well (generally not conducive conditions in lion habitat) and that humans create giant holes in the record by wiping out entire sub-population.
However, from genetic analyses of living lions and museum specimens, these authors have determined that there are two evolutionary groups of lions – those in India and Central/West Africa and those in Eastern/Southern Africa. This happens to have some interesting implications for lion conservation and reintroduction — check out the article!
The story of how reintroduced wolves transformed Yellowstone is now well known. According to the story, wolves scared elk away from the riversides, which allowed the willows and aspen to recover, allowing beavers to come back because they had home-building material big enough to use, and the beaver dams restored the health of the watershed.
I remember reading this story in college. I was sitting at a computer in UVA’s Alderman Library, digging up articles for a class presentation, when I stumbled on the now highly controversial article, “Wolves and the Ecology of Fear.” It blew my mind: right then and there, at the beginning of my last year of college, I knew I wanted to study how predators drove ecosystem dynamics.
It’s a beautiful story, and one that changed the trajectory of my career. And it’s one that’s been very hard to let go of, despite mounting evidence over the last decade that this story might not be more than a myth.
I had the good fortune of meeting Arthur at the Ecological Society of America talk last summer. I was a big fan of the work that he’d done, and that of his Ph.D. advisor, Dr. Matt Kaufman. But I didn’t envy either of them as they stepped into the fire of trying to take down what has become a beloved, monumental, epic tale. There’s no doubt that behaviorally-mediated trophic cascades do exist, and that predators can have profound influences on ecosystems, but the long-standing poster child for this simply isn’t real.
If you do one thing on your coffee break today, read his piece. While I could summarize the debate here, I couldn’t begin to do justice to Arthur’s eloquent argument.
Scratch that. If there’s one thing you do today, read Arthur’s piece. Not only will it make you think about wolves and ecology, but it will make you think about what nature we save, why we save it, and why that matters.
Season 7 is up and running, and we’re already running into some interesting critters!
No, that’s not a house cat wandering through the middle of the savanna. If you check out the gigantic ears, it appears that we’ve caught sight of a melanistic serval! Melanism involves a genetic mutation that causes the development of dark pigments in the skin, like a reverse albinism. For comparison, these guys normally look like this:
According to our field assistant, Daniel, the serval population is booming in the Serengeti at the moment. Perhaps with so many animals, it’s not entirely unexpected to find one or two odd cases like this.
Let’s see what other interesting finds pop up in the latest season of Snapshot Serengeti!
It’s been a long time coming, but it’s really and truly here.
First, we have to reiterate that Season 7 would never have been possible without your help. Last summer, our long-term National Science Foundation funding ran out and we were facing a gap in funding that could have closed down the camera survey forever. We launched the Save Snapshot Serengeti campaign to make sure that Season 7 happened — and because of your support, it did. Thank you, again, for making this project possible in so many ways.
And now! The hard won photos of Season 7 are here.
Season 7 is a big one, running from May 2013 all the way through to the end of November. That’s 7 months! In that time, the long rains faded and the roads became dusty. Stan, whose face you’ve seen so many times checking cams
began a master’s program in Dar es Salaam, and Norbert took over checking the cameras in his place.
Back in Minnesota, Margaret defended her dissertation and began a new post doc at Harvard working with the Phenocam project. I’ve been frantically analyzing data from Seasons 1-6 to finish my dissertation. And Meredith became the newest member of the Snapshot Serengeti team.
So, stop whatever you’re doing for a few minutes and go check out Season 7. Because really, whose day *isn’t* brightened by photos like this?
Watching animals in the wild, I’m always amazed at their power, agility, stealth, grace…I could go on. Even our household pets seem way more adept at maneuvering in this world than I feel on a daily basis.
And so this site
makes me giggle because it reminds me that I am not the only one who sometimes has trouble making her feet land where they are supposed to. Although I have to admit that if I ever tried to leap upwards ~10x my height, it would end far less gracefully than for these four-legged acrobats, who always seem to walk away completely unscathed.
This series of photographs documents a stand-off between two male lions — a younger male attacking? defending against? an older male. Interestingly enough, at the last minute, a pair of lionesses jump in and join the young male in evicting his older competitor.
In lion societies, males leave their birth prides at a young age and join together with other males, forming coalitions. These groups, which vary in size from 2-9 individuals, range across territories and attempt hostile takeover of established female prides from other males. While it may seem that the only obstacle to taking over a pride is the coalition of males who have already set up shop, it isn’t always in the females’ best interest to stand by passively and the males duke it out.
A group of male lions’ first order of business upon gaining tenure of a new pride is to off all the females’ dependent offspring. Loss of cubs brings females back into heat sooner, giving the new males a reproductive incentive to commit infanticide. The female, on the other hand, suffers an immediate loss in fitness — all the reproductive effort invested in her cubs is gone! Females have evolved a number of ways to reduce the risk of infanticide by males, including behavioral strategies such as banding together with their current coalition to stave off intruders. Is that what’s going on here? Perhaps, perhaps not. The female-defended male looks fairly young the be in this type of a situation. Cub loss, however, is an important factor to keep in mind when considering sport-hunting of mature male lions. The effect of removing a resident male is removed may cascade through his social group, leading to additional deaths within his pride when new males move in to his vacated niche.
Grinnell, J. and K. McComb. 1996. Maternal grouping as a defense against infanticide by males: Evidence from field playback experiments on African lions. Behavioral Ecology, 7(1): 55-59.
Packer, C., Scheel, D., and A. Pusey. 1990. Why lions form groups: food is not enough. American Naturalist, 136(1): 1- 19.
Last week I wrote about using really simple approaches to interpret camera trap data. Doing so makes the cameras a really powerful tool that virtually any research team around the world can use to quickly survey an ecosystem.
Existing monitoring projects in Serengeti give us a really rare opportunity to actually validate our results from Snapshot Serengeti: we can compare what we’re seeing in the cameras to what we see, say, from radio-tracking collared lions, or to the number of buffalo and elephants counted during routine flight surveys.
One of the things we’ve been hoping to do with the cameras is to use them to understand where species are, and how those distributions change. As you know, I’ve struggled a bit with matching lion photographs to known lion ranging patterns. Lions like shade, and because of that, they are drawn to camera traps on lone, shady trees on the plains from miles and miles away.
But I’ve finally been able to compare camera trap captures to know distributions for other animals. Well, one other animal: giraffes. From 2008-2010, another UMN graduate student, Megan Strauss, studied Serengeti giraffes and recorded where they were. By comparing her data with camera trap data, we can see that the cameras do okay.
The graph below compares camera trap captures to known densities of giraffes and lions. Each circle represents a camera trap; the bigger the circle, the more photos of giraffes (top row) or lions (bottom row). The background colors reflect known relative densities measured from long-term monitoring: green means more giraffes or lions; tan/white means fewer. For giraffes, on the whole, we get more giraffe photos in places that have more giraffes. That’s a good sign. The scatterplot visualizes the map in a different way, showing the number of photos on the y-axis vs. the known relative densities on the x-axis.
What we see is that cameras work okay for giraffes, but not so much for lions. Again, I suspect that this has a lot to do with the fact that lions are incredibly heat stressed, and actively seek out shade (which they then sleep in for 20 hours!). But lions are pretty unique in their extreme need for shade, so cameras probably work better for most other species. We see the cameras working better for giraffes, which is a good sign.
We’ve got plans to explore this further. In fact, Season 7 will overlap with a wildebeest study that put GPS collars on a whole bunch of migratory wildebeest. For the first time, we’ll be able to compare really fine scale data on the wildebeest movements to the camera trap photos, and we can test even more precisely just how well the cameras work for tracking large-scale animal movements. Exciting!
Hello! I haven’t written in a while. After I defended my dissertation in December, I’ve been busy getting ready to move to the Boston area. I have now started a research position (technically called a “postdoctoral fellowship”) at Harvard University.
In this new job, I am putting together a new citizen science project. This project will help scientists better understand and forecast the effects of climate change on North American trees and plants. We have cameras up throughout the U.S. and Canada taking automatic pictures of forests, grasslands, shrublands, desserts, and even tundra. There are already several years of images recorded, so it’s a great data set to play with.
In order to understand the seasonality of trees and plants, we talk about “phenology,” which is the timing of when trees and plants go through their various life stages. You can think about a maple tree, for example, which puts out leaf buds in the spring, grows those leaves into a full green canopy, then those leaves start to change color, and eventually they all fall off the tree in the autumn. These phenology events define all sorts of processes that are important to people – ranging from how much carbon trees and plants take out of the air to the timing of seasonal pollen release (which you might care about if you have allergies).
Of course, computer algorithms can only do so much, which is where citizen science comes in. The human eye is great at looking at fine details in images and figuring out what’s going on in strange images. For example, one of my colleagues was looking at a measure of greenness in grassland images from Hawaii. This measure was calculated automatically from the images. But something seemed strange. When he went and looked at the individual images themselves, he discovered that there was a common plant that flowered yellow all at once, which changed the greenness in a surprising way.
I’m excited about this new job, but I’m still involved with Snapshot Serengeti. These past couple months, Ali and I have been training Meredith on all the behind-the-scenes image and data processing that goes on both before you see the images and after you’ve classified them. This has slowed down the release of Season 7 (sorry), but ensuring continuity means fewer problems down the line. (By the way, Meredith is a fast learner – it’s just that there’s a lot to learn!) And I’ll still be blogging here periodically.
I’ve had a couple people ask about my dissertation. It’s now published and available online. Note, though, that it doesn’t contain any Snapshot Serengeti content. I was already rather far along in writing it when Snapshot Serengeti launched, so I didn’t have time to include it. We’re working on the first Snapshot Serengeti papers now, though, and we will be sure to let you know when they’re ready to read.
If you’ve got the time to sit down for 15 minutes and subject yourself to some truly awe-inspiring photography, check out this TED talk from the documentary film-making couple, Beverly and Dereck Joubert as they recount their adventures in Africa interacting with big cats and their big personalities.
Over the last few weeks, I’ve shared some of our preliminary findings from Seasons 1-6 here and here. As we’re still wrapping up the final stages of preparation for Season 7, I thought I’d continue in that vein.
One of the coolest things about camera traps is our ability to simultaneously monitor many different animal species all at once. This is a big deal. If we want to protect the world around us, we need to understand how it works. But the world is incredibly complex, and the dynamics of natural systems are driven by many different species interacting with many others. And since some of these critters roam for hundreds or thousands of miles, studying them is really hard.
I have for a while now been really excited about the ability of camera traps to help scientists study all of these different species all at once. But cameras are tricky, because turning those photographs into actual data on species isn’t always straightforward. Some species, for example, seem to really like cameras,
so we see them more often than we really should — meaning we might think there are more of that critter than there really are. There are statistical approaches to deal with this kind of bias in the photos, but these statistics are really complex and time consuming.
This has actually sparked a bit of a debate among researchers who use camera traps. Researchers and conservationists have begun to advocate camera traps as a cost-effective, efficient, and accessible way to quickly survey many understudied, threatened ecosystems around the world. They argue that basic counting of photographs of different species is okay as a first pass to understand what animals are there and how many of them there are. And that requiring the use of the really complex stats might hinder our ability to quickly survey threatened ecosystems.
So, what do we do? Are these simple counts of photographs actually any good? Or do we need to spend months turning them into more accurate numbers?
Snapshot Serengeti is really lucky in that many animals have been studied in Serengeti over the years. Meaning that unlike many camera trap surveys, we can actually check our data against a big pile of existing knowledge. In doing so, we can figure out what sorts of things cameras are good at and what they’re not.
Comparing the raw photographic capture rates of major Serengeti herbivores to their population sizes as estimated in the early 2000′s, we see that the cameras do an okay job of reflecting the relative abundance of different species. The scatterplot below shows the population sizes of 14 major herbivores estimated from Serengeti monitoring projects on the x-axis, and camera trap photograph rates of those herbivores on the y-axis. (We take the logarithm of the value for statistical reasons.) There are really more wildebeest than zebra than buffalo than eland, and we see these patterns in the number of photographs taken.
Like we saw the other week, monthly captures shows that we can get a decent sense of how these relative abundances change through time.
So, by comparing the camera trash photos to known data, we see that they do a pretty good job of sketching out some basics about the animals. But the relationship also isn’t perfect.
So, in the end, I think that our Snapshot Serengeti data suggests that cameras are a fantastic tool and that raw photographic capture rates can be used to quickly develop a rough understanding of new places, especially when researchers need to move quickly. But to actually produce specific numbers, say, how many buffalo per square-km there are, we need to dive in to the more complicated statistics. And that’s okay.