Notes from the Lab

Meredith: Not all of our exciting research takes place in the field — there’s a lot going on behind the scenes in our lab, and we rely on an invaluable group of undergraduate research assistants to help us go through the massive amounts of Snapshot data you guys provide! Jess has been working with us for the last few semesters and has some insight on what it’s like to work with this Serengeti data set. 

Hello everyone! My name is Jessica Dewey, and I am currently an undergraduate student at the University of Minnesota working in the research lab that runs this project! Cool, right? I’m new around here so I thought my first post should be an introduction of myself and how I got involved in this lab.

Imagine me a few years ago: a young high school student, undoubtedly procrastinating in some way, suddenly stumbling upon a website called “Snapshot Serengeti”. At the time, I was only certain of two things — I loved animals and I loved research – so this discovery was perfect for me! I spent most of my evening identifying animals, and continued to go back to procrastinate even more.

Now flash forward to last semester, when I get an email from one of the university biology clubs saying that Dr. Craig Packer, head of the Serengeti Lion Project, will be giving a talk about his work. Well I HAVE to go! I sit and listen intently, eager to learn all about research being done with lions. Near the end of his talk he then mentions a website (Snapshot Serengeti, of course) where all of the images from the field get uploaded for the public to identify, and I’m immediately floored. How did I get so lucky, to go to the very University that uses those pictures I spent time identifying years ago in their research? The best moment was when the graduate students working with Craig said that they were looking for undergraduates to help them with their research. I took the opportunity to introduce myself to Meredith, and so far my experience in this lab has been amazing.

I’ve learned a lot about how field research is done, how data is collected and analyzed, and what it takes for someone to actually be a researcher in the field.  Not everything I do is as fun as going through tons of pictures a day, but all of the work in this lab is interesting and meaningful, and that’s what really matters to me. One of the major projects the lab has been working on with Meredith is trying to characterize changes in habitat at the camera trap sites by looking at the Snapshot pictures. We have been going through the giant list of data to find pictures to use for this characterization. We haven’t been going through the images themselves – rather the metadata, or the data ABOUT the data (it’s literally the biggest Excel sheet I’ve ever seen). It can get monotonous at times, but what keeps me going is the thought that when we finish picking out all of these random images, we will get to look at them and use them for this research project.

I hope that was a thorough enough introduction for you all, but let me say one last thing: THANK YOU!  Without the time you all put in to identifying these pictures, the research we are doing would not be happening at the pace it is.

Here is one of my favorite images I’ve seen so far:

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Help us find animal selfies!

We’re partnering with National Geographic to put together a photo book of animal selfies from Snapshot Serengeti. We’ve got some selfies already from the first seven seasons, but because no one has looked through Season 8 yet, we don’t know what great selfies might be in there.

You can help! If you find an animal selfie, please tag it as #selfie in Talk. (Click the ‘Discuss’ button after you’ve classified the image and then enter #selfie below the image on the Talk page. You can get back to classifying using the button in the upper right.)

All proceeds from book sales will go to supporting Snapshot Serengeti. We’re planning for a fall 2016 publication date, so it will be a while. But we’re excited to get working on it.

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Season 8 Movie Time

Looks like everyone is sinking their teeth into Season 8! As a reminder, feel free to ask questions or chat with us through the Snapshot Serengeti Discussion board or in the comments of any of our blog posts.

Now, there’s some data from this new season that hasn’t made it online — sometimes, instead of taking pictures, our cameras accidentally switch into “video” mode and capture 10-second clips of animals doing their Serengeti thing. While this isn’t very good for us in terms of data collection (although we’ve been tossing around the idea of setting up a Snapshot Serengeti: Video Edition!…), it gives you a unique perspective on the lives of these animals.

(Okay, so it’s mostly animals eating grass. They eat a lot of grass. Perhaps not the most “unique” insight on their behaviors, but they’re still pretty fun to watch). Here’s some of my favorite accidental movies from our new Season!

 

(If you want more videos, Margaret has blogged some of her favorite movie clips from past seasons here and here)

Season 8 Release!

And now, the moment you’ve all been waiting for …  Can I present to you:

IMAG0335

SEASON 8!

I’m particularly proud of this, the first season that I’ve helped to bring all the way from the field to your computers. We’ve got a lot of data here, and I can’t wait for you guys to discover a whole host of exciting things in this new season.

This season is accompanied by IMPORTANT changes to our interface!

There’s a few more bits of data we think we can pull out of the camera trap photos this time around, in addition to all the great information we already get. One thing we’re particularly interested in is the occurrence of fire. Now, fire is no fun for camera traps (because they tend to melt), but these wildfires are incredibly important to the cycle of ecosystem functioning in Serengeti. Burns refresh the soil and encourage new grass growth, which attracts herbivores and may in turn draw in the predators. We have added a fire checkbox for you to tick if things look hot. Now, because we’re looking for things other than just animals, we replaced your option to click on “nothing there” with “no animals visible“, just to avoid confusion.

Some of the more savvy creature-identifiers among you may have noticed that there are a few Serengeti animals that wander into our pictures that we didn’t have options for. For this new season, we’ve added six new animal choices: duiker, steenbok, cattle, bat, insect/spider, and vultures. Keep an eye out for the following:

duikersteenbokcattle

batspidervulture

This season runs all the way from September 2013 until July 2014, when I retrieved them this summer, my first field season. Our field assistants, Norbert and Daniel, were invaluable (and inhumanly patient) in helping me learn to navigate the plains, ford dry river beds, and avoid, as much as possible, driving the truck into too many holes. Together, we set out new cameras, patched up some holes in our camera trap grid, and spent some amazing nights camped out in the bush.

Once I got the hang of the field, I spend my mornings running around to a subset of the cameras conducting a pilot playback experiment to see if I could artificially “elevate” the predation risk in an area by making it seem as though it were frequented by lions (I’m interested in the reactions of the lion’s prey, and to see whether they change their behaviors in these areas and how long it takes them to go back to normal). I’m more than a bit camera-shy (and put a lot of effort into carefully sneaking up around the cameras’ blind spots) but perhaps you’ll catch a rare glimpse of me waving my bullhorn around blaring lion roars…

Back in the lab, there’s been a multi-continental collaboration to get these data cleaned up and ready for identification. We’ve been making some changes to the way we store our data, and the restructuring, sorting, and preparing process has been possible only through the substantial efforts of Margaret, over here with me in the States, and Ali, all the way across the pond, running things from the Zooniverse itself!

But for now, our hard work on this season is over – it’s your turn! Dig in!

P.S. Our awesome developers have added some fancy code, so the site looks great even on small phone and tablet screens. Check it out!

Getting good data: part 1 (of many)

Despite the site being quiet, there’s a lot going on behind the scenes on Snapshot Serengeti at the moment. Season 8 is all prepped and currently being uploaded and should be online next week! And on my end, I’ve been busily evaluating Snapshot Serengeti data quality to try and develop some generalisable guidelines to producing expert quality data from a citizen science project. These will actually be submitted as a journal article in a special section of the journal Conservation Biology, but as that is a slowwwwwww process, I thought I’d share them with you in the meanwhile.
So! Recall that we use a “plurality” algorithm to turn your many different answers into a “consensus dataset” — this has one final answer of what is in any given image, as well as various measures of certainty about that image. For example, back in 2013, Margaret described how we calculate an “evenness score” for each image: higher evenness means more disagreement about what is in an image, which typically means that an images is hard. For example, everyone who looks at this photo
would probably say there is 1 giraffe , but we’d expect a lot more diversity of answers for this photo:
(It’s a warthog, btw.)
To test how good the plurality algorithm answers were, we created a “gold-standard dataset” by asking experts to identify ~4,000 images. Overall, we found that consensus answers from your contributions agreed with experts nearly 97% of the time!  Which is awesome. But now I want to take a closer look.  So I took all the images that had gold standard data and I looked at Evenness, Number of “nothing here” responses, and %support for final species, and evaluated how those measures related to whether the answer was right or wrong (or, impossible). Even though we don’t have an “impossible” button on the SS interface, some images simply are impossible, and we let the experts tell us so so these wouldn’t get counted as just plain “wrong.”
A note on boxplots: If you’re not familiar with a boxplot, what you need to know is this: the dark line in the middle shows the median value for that variable; the top and bottom of the boxes shows the 25 and 75 percentiles; and the “whiskers” out the ends show the main range of values (calculated as 1.5 * interquartile range, details here). Any outliers are presented as dots.
Evenness: The boxplot below shows the mean “Evenness” score described above vs. how the consensus answer matched the gold standard answer.   What you can see below is that the average evenness score for “correct” answers is about 0.25, and the average evenness score for wrong and impossible answers is 0.75. Although there are some correct answers with high evenness scores, there are almost no wrong/impossible answers with evenness scores below 0.5.
 EvennessVsAccuracy
Percent Support: This number tells us how many people voted for the final answer out of the total number of classifiers. So, if 9 out of 10 classifiers said something was a giraffe, it would have 90% support. It’s similar to evenness, but simpler, and it shows essentially the same trend. Correct answers tended to have more votes for what was ultimately decided as the final species.
PercentSupport
NumBlanks: So with the evenness and percent support scores, we can do a decent job of predicting whether the consensus answer for an image is likely to be right or wrong. But with Number of Blanks we can get a sense of whether it is identifiable at all. Margaret noticed a while back that people sometimes say “nothing here” if they aren’t sure about an animal, so the number of “nothing here” votes for an image ultimately classified as an animal also reflects how hard it is. We see that there isn’t a huge difference in the number of “nothing here” answers on images that are right or wrong — but images that experts ultimately said were impossible have much higher average numbers of “nothing here” answers.
 NumBlanksVsAccuracy
So, what does this tell us? Well, we can these metrics on the entire dataset to target images that are likely to be incorrect. In any given analysis, we might limit our dataset to just those images with >0.50 evenness, or go back through all those images with >0.05% evenness to see if we can come up with a final answer. We can’t go through the millions of Snapshot Serengeti images on our own, but we can take a second look at a few thousand really tricky ones.
There’s all sorts of cool analyses still to come — what species are the hardest, and what’s most often mistaken for what. So stay tuned!

Good news, bad news, good news, 2015 edition

It’s been quiet here on the blog, but we’ve been busy behind the scenes. In 2014, we revamped our data management procedures and structures. Season 7 — the one you finished classifying most recently — was the first where images and metadata were fully pre-processed and vetted before being sent to the Zooniverse. This pre-processing makes things much easier on us after we get all your classifications back from Zooniverse. But it does add some lead time.

Season 8 is the first good news. We’ve been pre-processing all December, finding weirdnesses like 84 images in a row all with the same timestamp, miscellaneous video files, timestamps from the future, and so forth. We are just about to start sending the images to Zooniverse, a processes which takes a few days. You should see Season 8 up within a couple weeks. We’ve also tweaked the interface a tiny bit. More on that soon.

The bad news is bad. After waiting since August for a reply from the National Science Foundation about our most recent grant proposal, we finally got it at the very end of December: declined. That means that we are again scrambling to find funds to keep the cameras rolling for 2015. And this time without much warning.

Season 8 is the first half of 2014 and Season 9 is the second half of 2014. Those are already in the bag. The cameras are rolling right now, and so there will be at least something of a Season 10. Worst case scenario is that we have to shut everything down for a while until we get more funding. But Craig is working hard to find interim funds.

The other good news is that we’ve been talking with some other Serengeti researchers who have set up a small camera trap survey in the western part of the ecosystem. They have tons of images and we’re talking with them about putting their images up on Snapshot Serengeti for classification. These images would be of new locations in the Serengeti and potentially a few new animal species. Could be a lot of fun. So even if there’s a pause in our image collection, hopefully we’ll have these other images to classify from the Serengeti that will be useful for ecological research.

Mystery Animal

I’ve been slogging through some field data today, when this mysterious critter crawled across my screen. These are the only shots we got – check out those zebra-stripey tube socks in the first image and the strange feathery texture. Who here knows what we snapped a pic of?

IMAG3392 IMAG3393 IMAG3394

Everybody dance now!

Formerly titled: I love the Zooniverse, and all its ridiculousness.

Some of you might be familiar with the silly manoeuvres we undertake to trigger the camera traps in the field. The sensors on the cameras are best at capturing movement side-to-side, hence ridiculousness such as this:

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The Stan Dance

and this…

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Craig surprising the camera

and this.

“The Ali Dance”

Well, fellow Zoo-ites Rob and Grant have now generously attempted their own version of this last one. Check it out on the Zooniverse Advent Calendar here!! And, if you’re feeling especially brave, send in your own recreation to team@zooniverse.org!

The predator of my predator is my friend.

If you’re a prey animal, you spend an awful lot of your time trying to not wind up like this:

lions_ngm_0813_006_brnd

 

As we’ve talked about an awful lot on this blog (here, here, and here, for example), the same holds true for a lot of predators. Just because you kill and eat other animals, doesn’t mean you don’t have to worry about being killed yourself (as this hyena so unceremoniously discovered).

But, what we haven’t talked so much about, is that the same holds true for plants. If you’re a plant, you get eaten by these terrifying animals:

But, just like prey animals and mesopredators can change their behaviour to minimise the risk of being killed, plants have a few tricks up their sleeves. They can spend a lot of energy growing big thorns, for example, that makes them less delectable.

Or! They can grow in places that their predators avoid — the places where their predators’ predators hang out. Got that?  It’s a trickle-down landscape of fear, which had, until now, only been really well documented in small experimental systems with critters like spiders and grasshoppers. But researcher Dr. Adam Ford and colleagues just published an elegant paper in Science showing that leopards and African wild dogs can make the Kenyan savanna less thorny through this cascade. Basically, leopards and wild dogs eat impala. Impala eat Acacia trees. Impala much prefer to eat acacias with fewer thorns (because really, who doesn’t?) – and, if given the opportunity, impala will can eat these small-thorned acacias so much that they can suppress the acacia population.

But! Leopards and wild dogs seem to be offering these tasty small-thorned acacias a refuge. Leopards and wild dogs spend most of their time in denser thickets, where they have more cover to hunt. Impala avoid these thickets and rarely venture in —  when they do, however, they have a much higher probability of being killed. And this creates this spiral – those tasty small-thorned trees survive and grow in these thickets because predators scare impala away.

So it’s a trickle down landscape of fear – a compelling and really exciting story. But, what sets Adam’s paper apart from many other attempts to document this effect in large predators, is the series of elegant experiments in which he and colleagues explicitly tested each step in this cascade.  Controlling for habitat use to confirm that impala aren’t getting killed in the woods simply because they spend more time there (and in fact, they get killed more even though the spend less time there). Adding and removing thorns to acacias to see if it was really the thorns that mattered. Creating herbivore exclosures to measure whether impala could really suppress acacia density. I spent my entire time reading the article alternating between saying “This is so cool!” and “I am so jealous!” It’s an amazing story. Read more about it here (or here, or or here)!

 

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