While we’re busy prepping the next NEXT PHASE of the Snapshot project (details coming soon!), we’ll be having guest posts from some of our invaluable undergraduate volunteers here in the University of Minnesota Lion Lab. They are writing a series on some of the lesser-known small animals which inhabit the Serengeti Park. Today, Lexi Vogler shares some information about the minuscule Klipspringer antelope:
The Klipspringer, or Oreotragus oreotragus, is a small antelope that lives on cliffs and rock outcrops in mid-eastern and southern Africa. This mammal weighs up to 18kg and can reach a height of 60cm. It stands on the tips of its hooves, which are adapted for steep and rocky terrains (such as kopjes). In this type of terrain and with their climbing and jumping abilities, the klipspringer can stray away from predators and can obtain an adequate food supply. These animals stand on the ends of their hooves, so they can easily stand with all four hooves close together, easily adapting to the rocky landscape.
Klipspringers have a specially insulated coat that can withstand freezing or extremely hot temperatures. They are extremely adaptable animals, including within their diet. They will feed on the vegetation that grows in between rocks in a kopje, as well as on leaves, shoots, succulents, berries, fruits, seedpods, and green grass. Klipspringers can typically obtain most of their moisture need through their food. They will travel up to 0.5km away from their shelter to forage for food during the dry season.
Socially, the klipspringer typically stays with one mate, and they share a permanent home or territory. They care and guard their offspring together, but it is rare to see two klipspringers make contact with one another. Instead, klipspringers will communicate through scent, sound and sight. They typically move and feed during the nighttime, and will lie in the shade in the afternoon when it starts to become hot.
Stay tuned to discover more interesting facts about these creatures and share in the comments if there are any animals you are particularly curious and would like to know more about!
This is another guest post by Drs. Tom Morrison and Michael Anderson about the Snapshot Serengeti Special Edition and what their research hopes to uncover.
Seeing the forest for the trees
First, a big THANK YOU to everyone who has helped classified images at Snapshot Serengeti, both past and present. Without the continued help of this great online community, our research would come to a grinding halt! So thank you. A number of folks (and at least one giraffe) have asked about the new study currently up on Snapshot Serengeti, so here’s a fuller explanation of this work.
Photos from our newest Snapshot Serengeti Special Season come from a camera trap experiment in Serengeti involving friends and collaborators based at Wake Forest University (US), University of Georgia (US) and University of Glasgow (UK).
One of the exciting things about these new images is that they come from some of the more remote corners of the park, far beyond where past photos (Season 1-9) were (and continue to be) collected. So, keep an eye out for different species than past surveys. For instance in the north, you might see oribi, a small and elegant ungulate with a large dark scent gland below its eye. In the south, our cameras overlap the home ranges of some of the few black rhinoceros still living in the park, and we already know there are at least a few rhino images in our pile, like this:
We set these cameras at a slightly higher height (1.5 meters in most cases), which allows us to see species from new wider angles. Admittedly, this new experimental design makes animal classifications a bit harder because we can often see far into the distance. Our advice is to simply do your best, but don’t sweat it too much if you can’t figure it out. Better to see the forest than the trees.
Back to the research…
Speaking of trees, this new study is trying to unravel the secret lives of trees. We monitor hundreds of individually marked trees around the ecosystem and revisit them each year to measure growth, survival, disease and few other things. You may have noticed little cages in some of the camera trap photos (see giraffe above). These are part of our experiment and enclose four small native tree seedlings which we transplanted to the plots after growing them in a nursery for 6 weeks. In fact we planted over 800 seedlings around the ecosystem to study the relative importance of herbivory, fire and rainfall on seedling growth and survival. So, we need camera traps to monitor things when we’re not there.
For example, check out the following sequence captured on one of our game cameras in southern Serengeti involving one of our marked trees:
What’s amazing about this is that not only does an elephant kill an adult tree, he does it under 60 seconds. This tree is an Acacia tortilis, or the “umbrella acacia,” named for its characteristic flat top. Umbrella acacias are one of the most common trees in Serengeti and one of our main study species. Images like these help inform our study of trees, telling us how they died, or at least how many large herbivores were in the area to potentially kill and eat them. But this begs the question: if a tree falls in the Serengeti, will anyone hear it? At least we know that there’s a small chance that one of our cameras might see it.
If you’ve been clicking through our new “extended survey” season, you might have noticed some new critters — particularly lots of cattle, sheep, goats, even donkeys! This is because the extended survey reaches much farther north, west, south, and east than the long-term camera study area. You can see all of Tom’s and Michael’s cameras on the map below in the black and gray circles. Two sites are at the edge of the park, where we might expect to see the occasional pastoralist passing by (even if they’re not supposed to) — and one site is *all* the way down deep in the Ngorongoro Conservation Area, where pastoralists live permanently. If you see any livestock in the images, go ahead and mark them as “cattle” — we’ll be able to figure it out from there!
Join us for a special edition of Snapshot Serengeti! Hot on the heels of Season 9, we’re launching the Snapshot Serengeti Extended Survey – a season loaded with photos from new locations in the Serengeti Ecosystem.
The original Snapshot Serengeti team has joined up with Prof. Michael Anderson (from Wake Forest University), Prof. Rico Holdo (from University of Georgia) and Dr. Tom Morrison (from University of Glasgow, UK) to understand how wildebeest, zebra and other herbivores impact vegetation dynamics in Serengeti.
We are particularly interested in understanding how the many many herbivores in Serengeti (both in terms of total number and the diversity of species) impact the growth, survival and germination of savanna trees. But why study trees? Savannas – like the one found throughout the Serengeti – present a bit of a paradox for ecologists. Most savannas receive sufficient rainfall throughout the year that they “should” become forests. We know that things like fire and herbivory help them maintain a mixture of continuous grasslands interspersed with smaller number of trees. Indeed, Serengeti receives enough rainfall (450mm per year at the driest part of the ecosystem to over 1000mm in the wettest) that it should be chocked full of trees, but yet, for most of the ecosystem, it remains a savanna (and quite a beautiful one).
We’ve designed an experiment understand the fate (the life and death and growth) of small trees at the seedling and sapling stage. During the wet season, we germinated a large number of seedlings in a nursery at the research center in Serengeti (see photo). We selected the two most dominant trees in the ecosystem: Acacia tortilis (commonly known as the umbrella acacia) and Acacia robusta (the stink bark acacia).
After growing 760 seedlings for about 6-8 weeks in the nursery, we transplanted them in the field at 19 different plots across the ecosystem, spanning the large gradient in rainfall mentioned above. Once in the ground, we subject them to various treatments (or mistreatments, if you like) such as fire, herbivory and watering. By using a combination of these different treatments, we hope to understand how important they are in determining the survival of trees at a small stage, which will ultimately inform why they survival into adulthood.
Our camera traps are set up at all of these different plots so we can track how herbivores use these areas differently and the effect that has on the vegetation, and we need your help to classify the photos! So head on over to Snapshot Serengeti and dive in! You never know what you might find!
It’s finally out! 9 long months ago, we received the good news that our second Snapshot Serengeti paper was accepted for publication in Conservation Biology as part of a special section on citizen science. Patience has never been my strong suit, so I’m overjoyed to announce that that special section is finally published!
The paper takes a pretty detailed look at how we turn your answers into our final dataset (that same one that was published in Nature Scientific Data last June). Remember that you guys are good, and even when you’re not sure about what you’re seeing, those wrong answers help us determine just how difficult an image is. My favourite demonstration of this is the boxplot below:
Now, I’ve written about this guy (and how to read boxplots) before. The gist of it is that we calculate a measure of disagreement across all of your answers for a given image. The disagreement score (also called evenness) ranges from 0 to 1, with 0 meaning that everyone agreed on what they saw and 1 meaning everyone said something different. You can see from the histogram on the right side of the plot that the vast majority of images were easy: everyone said the same thing! A good number of images are easy-ish, and a very small portion of the images are hard, with high disagreement scores.
When we compare images to experts, and look a the disagreement scores for images that were identified correctly or incorrectly, we see that images that were correct have generally lower disagreement scores (box on the left) than those that were incorrect (box on the right). That means we can use the disagreement score to predict whether images are probably right or wrong. If an image has a high disagreement score, it’s probably wrong or impossible, and we might want to have an expert review it before using it in an analysis.
For example. Across all images with disagreement scores 0-1, we know that 97% of images are correct. But say we want higher accuracy, so we set a threshold of images we accept and target for review. For example, 98.2% of images with a disagreement score of <0.75 are correct, so we could just accept all the ones with scores <0.75 and target all images >0.75 for review. Looking at the histogram to the right, that’s a pretty small percent of images needing a second look.
If 98.2% isn’t good enough, we can make that threshold stricter.
99.7% of images with disagreement scores <0.5 are correct, so we could set that as our threshold, and conduct an expert review of all images with scores above that. It’s still a relatively small number of images we need to look at.
Anyway. I know I’ve written about this before, but I think this really gets at the heart of why the Zooniverse/Snapshot Serengeti approach works for producing useable scientific data. And why your answers, even when you’re not confident in them, are so incredibly valuable. This approach means that ecologists and conservation biologists can engage volunteers like you on other camera trapping projects to tackle their own enormous camera trap datasets – enabling us to do bigger, broader research much faster.
As always, this wouldn’t be possible without your help. So thank you, again, for time and your clicks. And I can’t wait to see what you help us discover next!
One of the neat carnivores I got to work with in South Africa that we don’t experience much in Tanzania is the African wild (or “painted”) dog. These endangered carnivores live and hunt in highly social packs which, like wolves, are dominated by an alpha male and female. African wild dogs used to roam the Serengeti, but vanished in the park in the early 1990s due in part to diseases such as rabies and canine distemper contracted from domestic dogs.
During my first field season, I was fortunate enough to watch a pack of these animals being re-introduced into the park, and several more releases have taken place since. In total, over 60 wild dogs are now recolonizing Serengeti — we haven’t seen any in our camera trap areas yet, but there are rumors that they might be wandering through soon!
We promise that this one was worth the wait – hot-off-the-press Season 9 is one of our largest seasons yet, with over a million images (okay, okay, 300,000 some-odd “capture events”) to explore from our last year in the field! Head on over to www.snaphshotserengeti.org to join the search!
As we move through this new season, we’ll be updating regularly to share results that are emerging from our recent analyses, describe the brand new teaching tools we’re developing using this data to introduce young scientists to the world of research, and let you meet some of the branch new faces joining the Snapshot Serengeti team. Don’t forget to ask questions, post cool and unique pictures, and follow us on Facebook and through our Zooniverse discussion boards!
Thank you again for all of your effort in looking through these photos — this project wouldn’t be possible without you.
Thanks to you, we’ve already plowed through the entire “lost season” of Snapshot Serengeti. Granted, it was pretty small – only about 16,780 or so subjects, but you guys really knocked it out of the park.
So, first, let me say THANK YOU for your help!! It’s always such a thrill to announce a new season of data, and then watch that progress bar blast forward.
Second, speaking of progress bars, some of you have noticed that this progress bar went from zero to gone in, well, no time at all. Turns out we had a small bug in our code that meant we couldn’t see the progress bar advance. So finishing the lost season really took us by surprise too.
Third, if you feel like you’ve gone all excited by a new season to classify, only to have it ripped away, I promise that things will get better soon. We’ve now finished the major pre-processing for Seasons 9 and 10, and just need a couple weeks of minor pre-processing/porting to the Zooniverse. We’ll get them up online in early 2016 and you can classify to your heart’s content. Hopefully it will be something to cheer us all up as we return to work after the holidays. Seasons 9 and 10 are both really big, so there’s no risk you’ll miss out on them!
In the meanwhile, if you need an animal fix, Chicago Wildlife Watch could use your help!
Over the last few years, you’ve helped us identify almost 2 million photos (since there are often multiple frames per “subject”, that’s actually about 5.5 million individual frames!). And you might remember that a few months ago the first 1.2 million of those were just made available to researchers everywhere. But along the way, a few (thousand) photos fell through the cracks for one reason or another. Some sites didn’t get uploaded because of a quirk in the file structure. Some SD cards were corrupted and only months later the brilliant UMN IT department was able to recover the photos. Whatever the reason, we’ve accumulated 16,780 capture events that we’ve called “The lost season” — and we’ve added those images to the site today!
It’s a bit of a blast from the past — most of the images were taken in Seasons 3 and 4. But they’ve never been seen before…so go check it out! Who knows what you’ll find!
This week we have a guest post from herpetologist and Zooniverse volunteer Steve Allain (find him as “The Newt Guy” on Zooniverse), who has used Snapshot Serengeti data (available here) to dig a little deeper into our little-studied reptiles. Steve is a zoology graduate from Anglia Ruskin, Cambridge and has a particular passion and focus on British amphibian and reptile species. He is the current chairman of the Cambridgeshire and Peterborough Amphibian & Reptile Group (CPARG) where he helps to organise and coordinate a number of amphibian and reptile surveys around the county to map the distribution of amphibians within Cambridgeshire. More recently Steve has joined the IUCN SSC Amphibian Red Listing Authority as an intern.
In the summer of 2014 I visited Tanzania and went on a tour of the north of the country visiting such places as Arusha, Mount Meru, Ngorogoro Crater and the Serengeti. Before I went, I prepared myself for the wildlife I would encounter by helping out with the Snapshot Serengeti project. As a herpetologist (someone that studies amphibians and reptiles) I was not familiar with the mammalian fauna of Africa apart from the large and obvious animals that you are taught as a child. When I was in Africa, the identification skills I’d learnt through helping with the project really did pay off when it came to narrowing what species we had seen.
Recently I was reading a scientific paper regarding the monitoring of Komodo dragons using camera traps; this is an unusual method as reptiles generally don’t trigger camera traps due to their biology. I pondered some thoughts for a while and then it suddenly dawned on me that I knew of a project that had recently published a large amount of data from which I could filter out when reptiles had been captured by the camera traps. I decided to get in contact with some of the people involved with Snapshot Serengeti to help me get started.
One of the main questions that I have is when is the most likely time to capture a reptile on a camera trap, be it a snake or a lizard etc.? Is it in the morning or the afternoon? With the data published by the Snapshot Serengeti project I have been investigating this by first identifying all of the trapping events which contain reptiles. The original project identified 131 events which have been a good baseline to work from but with some extra digging I have identified another 120 events and I’m only just getting started.
Once I have a list of all of the trapping events, I intend to collate the data relating to my first question using time stamps as well as identifying which species are present. There are other questions which I am still formulating and so far most of the animals I’ve managed to identify have been species of rock lizard which like to bask on rocks and outcrops called kopjes. I’m hoping that my findings will be able to inform scientists in the future about the possibilities of using camera traps for studying the behaviour and distribution of reptiles over a large area.