So the new Snapshot Safari base camp for Snapshot Serengeti is a month old and teething problems aside all seems to be going well. I just wanted to take this opportunity to welcome all our new classifiers and to say a big thanks to all our old classifiers who have stuck with us. But most of all a massive thank you to our moderators who have worked so hard to make the transition run so smoothly. They have answered all your questions and queries without my back up due to the unfortunate timing of my own African field trip falling during the launch of Snapshot Safari.
It is not the first time Snapshot Serengeti has seen a big change. Some of you may remember its first outing as Serengetilive back in 2011. In those days things where a lot slower, you started classifying by first choosing an individual camera and working through it. There was an option to skip images, leaving them for someone else. Of course what ended up happening was all the hard to identify images and all the no animal grassy images were left to the end so that some people never got the chance to classify any animals.
We then progressed, in 2012, onto the Zooniverse platform and saw a huge change to the way things worked. Suddenly there was a lot more interaction between the scientists and the community. This was when the famous algorithms where developed by Margaret Kosmala and Ali Swanson and their team to act as a fail proof to anyone incorrectly identifying images.
We are all very grateful for their hard work and dedication that results in us classifiers being confident that our guesses won’t mess everything up.
So I hope that you are enjoying this third incarnation of Snapshot Serengeti and can be proud that it has worked so well over the years that it has spawned so many new projects.
My own field trip to Africa is coming to an end this week and I will be back in the land of internet connection. I will then hopefully be bringing you more regular posts and more updates on the project itself and how it is progressing. In the meantime don’t forget to check out our facebook and twitter pages.
The Snapshot team have written another paper using the Snapshot data we all help to classify. The paper A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle can be found at http://onlinelibrary.wiley.com/doi/10.1111/ele.12832/full for those of you interested in reading the original.
Lead by Meredith Palmer the paper explores how four ungulate species, buffalo, gazelle, zebra and wildebeest respond to predation risk during differing stages of the lunar cycle. These four make up the bulk of the African lion’s diet in the Serengeti along with warthog. Of course warthog are strictly diurnal so are not affected by the lunar cycle as they are tucked up nice and snug in a burrow.
For the other four night time can be a stressful time. None of these animals sleep all night, they snatch rest here and there, keep grazing and most importantly of all keep a watchful eye or ear out for possible attack.
It has long been thought that prey species territory is shaped by fear and that animals have safe areas (where they rest, give birth, etc) and risky areas where they instinctively know predators may be lurking. These areas trigger a risk versus reward response as they often contain better forage/water etc.
What Meredith and the team argue is that this landscape of fear is very much dynamic changing not only with seasons and night and day but on a very much finer scale as influenced by light availability through the moon.
Lions find it so much easier to hunt during nights where the moon gives of least light. It gives them a great advantage to stalking close to their prey using the dark as a kind of camouflage. The prey species, on the other hand, are at a distinct disadvantage, they can’t see the stalker and even if they sense its presence they are reluctant to flee as this presents a great risk in itself if they can’t see.
Meredith and her colleagues took the data from Snapshot Serengeti to quantify nocturnal behaviour of the key species using the presence or absence of relaxed behaviour (defined when we classify a species as resting or eating.) They then overlapped this with data collected through Serengeti Lion Project on lion density and hunting success. This data enabled them to work out what areas where high or low risk to the prey species. Using a clever statistical program, R, the data was analysed to see if lunar cycle had any bearing on animal behaviour, in particular, predator avoidance.
They found that moonlight significantly affected the behaviour of all four species but in a variety of ways. As we mentioned before there is often a good reason to venture into the high risk areas and the trade off in increased risk of predation is a really good feed. Buffalo for instance don’t change their use of space so much but were found to form more herds on dark nights. It seems safety in numbers works well for buffalo. Zebra react similarly in their herding activity but are much more erratic when it comes to space use, moving around a lot more randomly keeping potential predators on their toes.
Each species showed an aversion to using high risk areas at night but, particularly wildebeest and zebra, were found to increase their use of these areas when the moons luminosity was higher and safety increased. It was noted that high risk areas where avoided more frequently in the wet season than the dry. The thought being that there is increased hours of moonlight during the dry season that the animals take advantage of. Perhaps too the drive to find enough good food is a factor.
This paper serves to remind us that although what we do at Snapshot Serengeti is fun it is more than just a way for us classifiers to pass the time. It really has a very significant role in science and that role is ever increasing.
Some of you will have noticed that our progress bar on season 10 has not been showing any progress. Well it turns out that we have made loads of progress, it’s just the bar that was not getting anywhere.
The good folks at Zooniverse have fixed it for us and you will now see we are about half way through season 10 which is fantastic. There are just under 700 000 images to classify this season so thanks to you, are dedicated team of citizen scientists we have around 350 000 left to go. That’s 350 000 chances of finding that one image you have been waiting for. I have noticed recently lots of you posting on talk that you have classified your first ‘waterbuck’ or ‘serval’. If you haven’t discovered your dream find yet there is still time and yes there is a season 11 in the wings.
Whilst on the subject of talk I wanted to gently remind everyone of a few etiquette points.
#Hashtags, love ‘em or hate ‘em they are part of social media and they are not going away. On Snapshot Serengeti we use them for a specific reason and that is to help others to search for and find certain images.
If you have found a great image that you think others will want to see and you are certain of the species then go ahead and hashtag it, but, if you find an image that you are not sure of then please don’t hashtag it with your guess. You can still put the pictures up in talk for discussion and perhaps someone else will be along who is positive about the id and can then hashtag it. Basically, please use hashtags thoughtfully.
Which brings me to another point; if you can’t identify an image and you post it up for discussion always give us your best guess. No one will laugh; it’s what makes it fun seeing what other people make of the images when you are really stumped. Many a time I have confidently shared a tricky image almost certain for instance it’s a long sort after rhino only to have someone else’s eyes point out that if I look a bit closer that actually it is a rock! Even our expert modifiers get things wrong occasionally and are reluctant to confidently make a call on certain images. Some of them are just so darn impossible to id. So just give it your best shot, it’s what everyone else does.
The main aim is to enjoy yourself, challenge yourself and use other peoples experience when yours fails you. The Snapshot family of classifiers and moderators is a dedicated and knowledgeable bunch and as I have said before, this project would not exist without you all. Keep up the great work one and all.
Meredith has been busy this past week attending the Citizen Science conference in St Paul, Minnesota. She reports back that it was a fantastically stimulating conference that confirms the high esteem that citizen science has grown within the science community.
The yearly conference sees a diverse group of people from researchers, educators and universities to the likes of NGO’s and museums get together to discuss the use and promotion of citizen science. Although we at Snapshot Serengeti have been aware of its great impact for some time citizen science is now emerging and is recognised as a powerful tool in the advancement of research by many.
Those attending the four day event collaborated by sharing their varied experience and ideas on a variety of topics. The collection and sharing of data and how to impact policy was discussed. There was focus on how to use citizen science as an engaging teaching tool, how to bring citizen science to a wider audience and how to involve citizens more in research. Those attending brought their joint experience and expertise together to discuss how citizen science impact on science could be measured and evaluated. If you want to find out more about the conference then visit this link.
We sometimes forget when working away at classifying our stunning images on Snapshot Serengeti that there is a lot of tech going on that enables us citizen scientists to be of use to the scientists. Meredith gave what’s known as a ‘project slam’ essentially a 5 minute presentation about our work on Snapshot Serengeti and how it has paved the way for helping other cameratrap citizen science projects. A quick look around Zooniverse will show just how many there are now.
The massive amount of data produced over several seasons through Snapshot Serengeti have allowed the development of a robust, tried and tested methodology that smaller projects would have taken years longer to develop. Just contemplate the work that went into developing interfaces, protocols, pipelines and algorithms for taking millions of classifications of untrained volunteers and turning them into a dataset which has been verified to be >97% accurate.
It is awesome to see that something we all find so truly engaging can translate into such serious stuff in the field of science. I think we, the citizen scientists, and the Snapshot team can be rightly proud of our work on this brand new branch of science
Here is another pair of antelope that are often muddled up on Snapshot Serengeti; topi and hartebeest. These two share a similar size and body shape and for those of you not familiar with them they can prove a bit tricky.
Topi and hartebeest belong to the same tribe, Alcelaphini, which also includes wildebeest. These antelope typically have an elongated face, long legs, short necks and stocky bodies. Although these antelope have reasonably large bodies their long legs mean they have retained the ability to run fast, a good adaptation for life on the open plains. It is believed that the long face developed in place of a long neck in order to reach the grasses they consume.
There are several species of both topi and hartebeest in Africa, two are found in the Serengeti. Coke’s hartebeest or kongoni (Alcelaphus cokii) are selective grazers with browse making up less than 4% of their diet. Serengeti topi (Damaliscus jimela) are 100% grazers
In both species males are territorial but topi also form leks from which to display to passing females. Males holding territory close to the lek are more desirable to females. Dominant females will actively prevent subordinate females from mating with these males.
So side by side we can see that the topi is much darker coloured than the hartebeest with distinct sandy socks up to its knees and conspicuous black patches on the thighs and shoulders. In contrast the hartebeest has pale legs and underbelly with a darker upper body. The paleness forms a patch on the top of the thigh.
From behind the contrast between leg colour and backside is very obvious with topi sporting dark legs with pale rump and back and hartebeest pale legs and rump with dark back.
Horn shape is also different. A topi’s horns sweep up and back whereas a heartebeest’s sweep out to the side before kinking back. They also sit on a prominent bony ridge on the top of the head.
Hopefully this will help you tackle all the images waiting on season 10.
So we all know there are millions of images on snapshot Serengeti and that it is us citizen scientists who do all the work classifying them. The scientists can then get on with the task of figuring out what’s going on out there in the animal kingdom, hopefully in time to save some of it from our own destructive nature.
But… have you spared much thought as to how the images go from over 200 individual camera-traps dotted around the Serengeti to the Zooniverse portal in a state for us to start our work.
Firstly the SD cards have to be collected from the cameras and as this is an ongoing study replaced with fresh SD cards. This is done about every 6 to 8 weeks. A camera traps batteries can actually go on performing far longer than this but as the field conditions can be tough you never know when a camera may malfunction. This time frame is a good balance between not ending up with months worth of gaps in the data and not spending every minute in the field changing cards.
The team are able to check about 6 to 10 sites a day so with 225 cameras in play it takes around a month just to get to each site. Mostly the cameras are snapping away happily but there are always some that have had encounters with elephants or hyena but actually some of the most destructive critters can be bugs, they like to make nests of the camera boxes. As well as checking the cameras themselves the sites need to be cleared of any interfering foliage, we all know how frustrating a stray grass blade can be.
So with a hard drive full of all the data it then has to wait for a visiting field researcher to hand carry it back to the University of Minnesota, USA. It means the data is only received every 6 months or so but it is far safer than trusting the post. Once safely received it is up to Meredith to start the painstaking work of extracting the date time stamps. As sometimes happens there are glitches and she has to fix this by figuring out when the camera went off line or when capture events got stuck together. She says it is much like detective work. The images are then assigned codes and stored on the Minnesota Supercomputer Institute (MSI) servers.
Once it is all cleaned up and backed up it is sent to the Zooniverse team who then format it for their system giving new identifiers to each image. Finally it is ready for release to all the thousands of classifiers out there to get to work on.
So as you can see it really is a team effort and a massive under taking. It is no good collecting tonnes of data if there is no one with the time to do anything with it. I will take this opportunity again to thank you for all your help with the project. Keep up the good work.
Okay, so by now you’ve heard dozens and dozens of times that you guys produce really good data: your aggregated answers are 97% correct overall (see here and here and here). But we also know that not all images are equally easy. More specifically, not all species are equally easy. It’s a lot easier to identify a giraffe or zebra than it is to decide between an aardwolf and striped hyena.
The plot below shows the different error rates for each species. Note that error comes in two forms. You can have a “false negative” which means you miss a species given that it’s truly there. And then you can have a “false positive,” in which you report a species as being there when it really isn’t. Error is a proportion from 0 to 1.
We calculated this by comparing the consensus data to the gold standard dataset that Margaret collated last year. Note that at the bottom of the chart there are a handful of species that don’t have any values for false negatives. That’s because, for statistical reasons, we could only calculate false negative error rates from completely randomly sampled images, and those species are so rare that they didn’t appear in the gold standard dataset. But for false positives, we could randomly sample images from any consensus classification – so I gathered a bunch of images that had been identified as these rare species and checked them to calculate false positive rates.
Now, if a species has really low rates of false negatives and really low rates of false positives, then it’s one people are really good at identifying all the time. Note that in general, species have pretty low rates of both types of error. Furthermore, species with lower rates of false negatives have higher rates of false positives. There aren’t really any species with high rates of both types of error. Take rhinos, for example: folks often identify a rhino when it’s not actually there, but never miss a rhino if it is there.
Also: we see that rare species are just generally harder to identify correctly than common species. The plot below shows the same false negative and false positive error rates plotted against the total number of pictures for every species. Even though there is some noise, those lines reflect significant trends: in general, the more pictures of an animal, the more often folks get it right!
This makes intuitive sense. It’s really hard to get a good “search image” for something you never see. But also folks are especially excited to see something rare. You can see this if you search the talk pages for “rhino” or “zorilla.” Both of these have high false positive rates, meaning people say it’s a rhino or zorilla when it’s really not. Thus, most of the images that show up tagged as these really rare creatures aren’t.
But that’s okay for the science. Because recall that we can assess how confident we are in an answer based on the evenness score, fraction support, and fraction blanks. Because such critters are so rare, we want to be really sure that those IDs are right — but because those animals are so rare, and because you have high levels of agreement on the vast majority of images, it makes it really easy to review any “uncertain” image that’s been ID’d as a rare species.
Pretty cool, huh?
And now, the moment you’ve all been waiting for … Can I present to you:
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:
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!
As I’m writing up my dissertation (ahh!), I’ve been geeking out with graphs and statistics (and the beloved/hated stats program R). I thought I’d share a cool little tidbit.
Full disclosure: this is just a bit of an expansion on something I posted back in March about how well the camera traps reflect known densities. Basically, as camera traps become more popular, researchers are increasingly looking for simple analytical techniques that can allow them to rapidly process data. Using the raw number of photographs or animals counted is pretty straightforward, but is risky because not all animals are equally “detectable”: some animals behave in ways that make them more likely to be seen than other animals. There are a lot of more complex methods out there to deal with these detectability issues, and they work really well — but they are really complex and take a long time to work out. So there’s a fair amount of ongoing debate about whether or not raw capture rates should ever be used even for quick and dirty rapid assessments of an area.
Since the Serengeti has a lot of other long term monitoring, we were able to compare camera trap capture rates (# of photographs weighted by group size) to actual population sizes for 17 different herbivores. Now, it’s not perfect — the “known” population sizes reflect herbivore numbers in the whole park, and we only cover a small fraction of the park. But from the graph below, you’ll see we did pretty well.
Actual herbivore densities (as estimated from long-term monitoring) are given on the x-axis, and the # photographic captures from our camera survey are on the y-axis. Each species is in a different color (migratory animals are in gray-scale). Some of the species had multiple population estimates produced from different monitoring projects — those are represented by all the smaller dots, and connected by a line for each species. We took the average population estimate for each species (bigger dots).
We see a very strong positive relationship between our photos and actual population sizes: we get more photos for species that are more abundant. Which is good! Really good! The dashed line shows the relationship between our capture rates and actual densities for all species. We wanted to make sure, however, that this relationship wasn’t totally dependent on the huge influx of wildebeest and zebra and gazelle — so we ran the same analysis without them. The black line shows that relationship. It’s still there, it’s still strong, and it’s still statistically significant.
Now, the relationship isn’t perfect. Some species fall above the line, and some below the line. For example, reedbuck and topi fall below the line – meaning that given how many topi really live in Serengeti, we should have gotten more pictures. This might be because topi mostly live in the northern and western parts of Serengeti, so we’re just capturing the edge of their range. And reedbuck? This might be a detectability issue — they tend to hide in thickets and so might not pass in front of cameras as often as animals that wander a little more actively.
Ultimately, however, we see that the cameras do a good overall job of catching more photos of more abundant species. Even though it’s not perfect, it seems that raw capture rates give us a pretty good quick look at a system.