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A different way to see the world

I posted a little while ago about the applications of remote sensing technology in biological research. Here’s a TED talk by one of the authors of the South African study I mentioned with some fascinating visuals showing the level of detail these technologies can reveal to us. While the talk starts off flying you through a “lion’s-eye” view of hunting terrain, Greg Asner goes on to reveal some of the other ecological and conservation implications of these technologies and how they can help us do things from finding illegal goldmines and documenting species composition in the Amazon to tracking habitat changes by elephants and fire back in Africa.


South Africa, here I come.

Back in October, I wrote about how a grant proposal was turning me into a zombie.

Well, much to my surprise, turns out that my foray into the world of the walking dead was worth the effort. I’ve just heard that the National Science Foundation does, indeed, want to send me to South Africa to carry out this research!

Basically, I’m interested in how the other big carnivores (hyenas, leopards, cheetahs, and wild dogs) manage to live with lions. And I think that one of the keys to their coexistence has to do with how the other carnivores distribute themselves across the landscape to avoid being killed or harassed by lions. Do they avoid huge tracts of land and lose access to the valuable resources within? Or are they able to fine-tune their behavior and still use those areas without getting into trouble?

As you know, I’m using the camera traps to try and figure out these patterns of habitat use by the major carnivores. But that still just tells me what they do in a place (the Serengeti) where there are lions, and I don’t know if the lions are directly causing these patterns. I can’t, for obvious reasons, do an experiment where I take out all the lions and see if the rest of the animals change their behaviors, which would help me identify such a causal relationship.

But in South Africa, there are two virtually identical reserves — they have the same habitat, the same prey animals, and the same carnivores…except that one has lions and one does not. These reserves are right next to each other and surrounded by fencing. So they are pretty much the perfect experimental system where I can actually answer whether or not the patterns we see in predator behavior are caused by lions. What’s even better is that there are already ongoing research projects there that are running camera trap surveys very similar to Snapshot Serengeti. So most of my work will be doing some measurements of the vegetation and working with the researchers in South Africa to compile their data in a way that we can draw these comparisons.

Comparison of Serengeti (left) and Phinda/Mkhuze (right).

Comparison of Serengeti (left) and Phinda/Mkhuze (right). Phinda and Mkhuze are two virtually identical reserves in South Africa, except that lions have historically been excluded from Mkhuze.

It’s going to be a *lot* of computer work with a *little* bit of getting out into the bush, but the questions are so cool and the ability to effectively isolate the effect of a single top predator (lions) in a natural ecosystem is so rare, that I couldn’t be more excited about it.

The History of Lions

Barbary Lion, BBC

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!


What we’ve seen so far, Part IV

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.

Ingela scanning for lions from the roof of the car.

Ingela scanning for lions from the roof of the car.

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!

What we’ve seen so far, Part III

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.

Find that nest!

You’ve got to check out this game:

Screen Shot 2014-03-16 at 3.08.08 PM

Scientists from the University of Exeter are trying to understand camouflage. Specifically, they want to understand how camouflage helps protect animals from being eaten for dinner, and they’re doing this by studying ground nesting birds in South Africa & Zambia.

Like Snapshot Serengeti, these guys use camera traps too, to figure out whose munching on birds and their nests. Unlike Snapshot Serengeti, however, they aren’t asking for help IDing the photos: instead, they’re asking for help figuring out how predators see, and how different types of camouflage work better or worse against predators with different types of vision.

Humans have trichromatic vision, meaning we have three different types of receptors (light sensitive cells in the eye) that can process color: red (longwave), green (mediumwave), and blue (shortwave). Some animals only have two receptor types and can only see one or two colors, whereas other animals have four, allowing them to see wavelengths such as infrared or ultraviolet that are invisible to people.  Thus, what camouflages eggs against one predator might not work so well against another predator.

What these researchers have done is create a game that mimics the vision of other predators. So you get to see the world through the eyes of either a genet cat (with dichromatic vision) or a vervet monkey (with trichromatic vision), and “hunt” for birds or their nests in a series of pictures. This helps scientists understand how perception changes among different animals, and how camouflage works against different perception types.

So go check it out! But don’t forget to come back and then help us classify Season 7! We’ll announce its debut on the blog soon!


Data from Afar

Earth, rendered from MODIS data

Look at this picture of the world – it’s blue, it’s green, it’s dynamic. It is covered in swirling clouds beneath which we can see hints of landforms, their shapes and their colors. Satellites tireless orbiting the Earth gathered the information to construct this image. And every pixel of this this awe-inspiring rendition of our planetary home is packed with data on geology, topography, climatology, and broad-scale biological processes.

I still find it funny that I can sit in my office and watch weather patterns in Asia, cloud formation over the Pacific, or even examine the contours of the moon in minute detail, thanks to remote sensing programs. Not that lunar geomorphology is particularly pertinent to lion behavior, at least, in any way we’ve discovered so far. Still, an incredible amount of information on the Serengeti landscape can be collected by remote sensing and incorporated into our research. “Remote sensing” simply refers to gathering information from an object without actually making physical contact with the object itself. Primarily, this involves the use of aerial platforms (some kind of satellite or aircraft) carrying sensor technologies that detect and classify objects by means of propagated signals. Most people are passingly familiar with RADAR (“radio detection and ranging”) and SONAR (“sound navigation and ranging”), both examples of remote sensing technologies where radio waves and sound, respectively, are emitted and information retrieved from the signal bouncing back off of other objects. The broad-scale biotic or abiotic environmental information gathered can then be used in our analyses to help predict and explain patterns of interest. People are using remote sensing to monitor monitoring deforestation in Amazon Basin, glacial features in Arctic and Antarctic regions, and processes in coastal and deep oceans. Here are brief vignettes of several kinds of remote sensing data we draw upon for our own biological studies.

Herbivore distributions overlaid on NDVI readings

Herbivore distributions overlaid on NDVI readings

NDVI: Normalized Difference Vegetation Index

NDVI is collected using the National Oceanic and Atmospheric Administration (NOAA)’s Advanced Very High Resolution Radiometer and is an assessment of whether a bit of landscape in question contains live green vegetation or not. And yes, it’s far more complicated than simply picking out the color “green”. In live plants, chlorophyll in the leaves absorbs solar radiation in the visible light spectrum as a source of energy for the process of photosynthesis. Light in the near-infrared spectral region, however, is much higher in energy and if the plant were to absorb these wavelengths, it would overheat and become damaged. These wavelengths are reflected away. This means that if you look at the spectral readings from vegetation, live green plants appear relatively dark in the visible light spectral area and bright in the near-infrared. You can exploit the strong differences in plant reflectance to determine their distribution in satellite images. Clever, right? NDVI readings are normalized on a scale of -1 to 1, where negative values correspond to water, values closer to zero indicate barren areas of tundra, desert, or barren rock, and increasingly positive values represent increasing vegetated areas. As you can see in the image above, we have NDVI readings for our study sites which can be used to examine temporal and spatial patterns of vegetation cover, biomass, or productivity — factors important in driving herbivore distribution patterns.

Wildfire occurrence data gathered from MODIS satellites

MODIS: Moderate-resolution Imaging Spectroradiometer

The MODIS monitoring system is being carried in orbit aboard a pair of satellites, the Terra and Aqua spacecraft, launched by NASA in the early 2000s. The two instruments image the entire surface of the Earth every 1 to 2 days, collecting measurements on a range of spectral bands and spatial resolutions. Their readings provide information on large-scale global processes, including pretty much anything that can occur in the oceans, on land, or throughout the lower atmosphere. Many of the beautiful Earth images, such as the one at the head of this post, are constructed using MODIS data. We hope to use MODIS information for the detection and mapping of wildlife fires, which impact organisms at every level of the Serengeti food web.

LiDAR: Apparently, a common misnomer is that “LiDAR” is an acronym for Light Detection and Ranging, while the official Oxford English Dictionary (the be-all-end-all for etymology) maintains that the word is merely a combination of light and radar. Either way, it’s less of a mouthful than the other two techniques just discussed!

LiDAR is quite well-known for its applications in homing missiles and weapons ranging, and was used in the 1971 Apollo 15 mission to map the surface of the moon. We also use this for biology, I promise. What LiDAR does, and does far better than RADAR technology, is to calculate distances by illuminating a target with a laser and measuring the amount of time it takes for the reflected signal to return. High resolution maps can be produced detailing heights of objects and structural features of any material that can reflect the laser, including metallic and non-metallic objects, rocks, rain, clouds, and even, get this, single molecules. There are two types of LiDAR: topographic, for mapping land, and bathymetric, which can penetrate water. To acquire these types of data for your site, you load up your sensors into an airplane, helicopter, or drone and use these aerial platforms to cover broad areas of land. I first became aware of LiDAR from a study that used this technology in South Africa to map lion habitat and correlate landscape features with hunting success. I’ve also seen it used to map habitat for wolves and elk, determine canopy structure, and, interestingly enough, to remotely distinguish between different types of fish (weird, and also really neat). Now we don’t have LiDAR information for the Serengeti, so keep an eye out for anyone who might be able to lend us a couple of small aircraft and some very expensive sensing equipment!

What we’ve seen so far, cont’d.

Playing with data is one of the many things I love about research. Yes, it is super nerdy. I embrace that.

Last week I shared with you the various critters we’re getting to *see* in the Snapshot Serengeti data. Over 100,000 wildebeest photos! Over 4,000 lions! And the occasional really cool rarity like pangolins


and rhinos.


But the photographs carry a lot more information than just simply what species was caught in the frame. For example, because the photos all have times recorded, we can see how the Serengeti changes through time.

This graph shows the number of daily pictures of wildebeest and buffalo, and how the daily capture rates change through the seasons. Each set of bars represents a different month, starting in July 2010. Wildebeest are in dark green, buffalo in light green. The y-axis is on a square-root scale, meaning that the top is kind of squished: the difference from 30-40 is smaller than the distance from 0-10. Otherwise, we’d either have to make the graph very very tall, or wouldn’t really be able to see the buffalo counts at all.


Buffalo are captured more-or-less evenly across the different months. But the wildebeest show vast spikes in capture rates during the wet season. These spikes in numbers coincide with the migration, when the vast herds of wildebeest come sweeping through the study area.

Now, the number of photos doesn’t directly transfer into the number of wildebeest in the study area, and these aren’t changes in population size, but instead changes in distribution of the wildebeest. But it’s pretty cool that with something as simple as just the number of photographs, we can see these huge changes that accurately reflect what’s going on in the system.

What we’ve seen so far…

As we prepare to launch Season 7 (yes! it’s coming soon! stay tuned!), I thought I’d share with you some things we’ve seen in seasons 1-6.

Snapshot Serengeti is over a year old now, but the camera survey itself has been going on since 2010; you guys have helped us process three years of pictures to date!

First, of the >1.2 million capture events you’ve looked through, about two-thirds were empty. That’s a lot of pictures of grass!


But about 330,000 photos are of the wildlife we’re trying to study.  A *lot* of those photos are of wildebeest. From all the seasons so far, wildebeest made up just over 100,000 photos! That’s nearly a third of all non-empty images altogether.herbivores

We also get a lot of zebra and gazelle – both of which hang out with the wildebeest as they migrate across the study area. We also see a lot of buffalo, hartebeest, and warthog — all of which lions love to eat.


We also get a surprising number of photos of the large carnivores. Nearly 5,000 hyena photos! And over 4,000 lion photos! (Granted, for lions, many of those photos are of them just lyin’ around.)

Curious what else? Check out the full breakdown below…

Preview of “Tables.xlsx”

The surprisingly powerful role of fear


An unpleasant emotion caused by the belief that someone or something is dangerous, likely to cause pain, or a threat: — Oxford English Dictionary

 Fear is an emotion induced by a perceived threat which causes entities to quickly pull far away from it and usually hide. — Wikipedia

 To be afraid of (something or someone). To expect or worry about (something bad or unpleasant). To be afraid and worried. — (Not very helpful)


Both Meredith and I have talked a bit about the meaning and role of “fear” in shaping animal behaviors and population dynamics. The word “fear” is a bit touchy. When ecologists use the word fear, we aren’t talking about the emotion as you and I know it. We are referring to a certain type of situation and response. For example, lions kill and eat wildebeest. This creates “landscape of fear” – meaning that the wildebeest exists in a landscape in which certain physical places have a higher risk of predation. You can envision that this landscape has its own topography — hills and valleys of high and low risk. The differing levels of risk can trigger physiological responses as well as behavioral responses. For example, wildebeest may show higher levels of stress hormone in the “risky” areas, or they may avoid “risky” areas even though that’s where the best food is.

This is what we mean when we talk about fear. We are not talking about whether the wildebeest lies awake at night dreaming bad dreams. We are talking about situations of high and low risk and the physiological and behavioral responses.

That being said, “fear” is an incredibly powerful driving force in the natural world. I’ve touched on this from time to time. The idea that smaller predators are so desperate to avoid being beaten up by the big guys, that they avoid the areas with the best food or den sites, and their populations decline even if they aren’t actively being killed by the big guys. This process still amazes me. Even cooler? Fear doesn’t just matter for big and small predators, it doesn’t just matter for predators and prey. The effects of fear can trickle down from predators to prey to plants, just like the trophic cascades I wrote about last week.

Some of my favorite research on the role of fear in trophic cascades has been done by researchers out of the Schmitz Lab at Yale University. 

In 1997, Os Schmitz and his students hypothesized that predators could trigger trophic cascades not just by killing and eating herbivores, but by scaring herbivores and changing their behaviors. Os, in his infinite wisdom, works in systems that are experimentally tractable. So he and his team got a bunch of spiders (their predator) and grasshoppers (their prey) and did an experiment that I will never ever be able to do with lions. They created two treatments: a risk treatment, where the spiders had their pincers glued shut and couldn’t kill the grasshoppers, and a predation treatment, where the spiders got to carry on with all the spidery things they like to do (such as eat grasshoppers). They put the grasshoppers in with one of the two types of spiders, and compared what happened.

How spiders affect grasshoppers affect plants. Excerpted from Schmitz et al. 1997.

How spiders affect grasshoppers affect plants. Excerpted from Schmitz et al. 1997.

So, perhaps unsurprisingly, grasshoppers were afraid of spiders whether or not the spiders had their mouths glued shut. In the presence of any spider, grasshoppers changed their diet to avoid areas that spiders liked to lurk, spent less time eating, and only really came out to eat when the spiders were sleeping. The surprising thing is that these behaviors resulted in lower grasshopper densities irrespective of whether or not the spiders could kill grasshoppers. The presence of spiders with their mouths glued shut changed the behavior of the grasshoppers, which resulted in the grasshoppers acquiring less food, which in turn decreased grasshopper populations. What’s more, these effects trickled down to the plant communities. Grasshoppers eat grass, but mere presence of predatory spiders can reduce the effect of grasshoppers on this grass.

Since this 1997 experiment, Os’s lab has gone on to produce some of my favorite research on the role of fear in driving ecological systems. Now if only I could figure out how do  such enlightening experiments in the Serengeti…

Reference: Schmitz, O.J., Beckerman, A.P. & O’Brien, K.M. (1997) BEHAVIORALLY MEDIATED TROPHIC CASCADES: EFFECTS OF PREDATION RISK ON FOOD WEB INTERACTIONS. Ecology, 78, 1388–1399.


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