Snapshot Gems
Whilst we wait for season 12 of Snapshot Serengeti to be launched we have put together some of the great images from the previous seasons. Snapshot Serengeti has been going for eight years with its 200+ camera-traps never sleeping (except for the occasional malfunction/animal destruction). Its just possible that you may have missed some of these great images during that time, so sit back and enjoy.
Machine learning and citizen science…a winning combination!
Hartebeest through a dislodged camera-trap cover.
* Sarah Huebner, who heads up the Snapshot Safari team has written the following blog to give all participants of Snapshot Safari projects the low down on machine learning advances that are being introduced today*
In the era of Big Data, when equipment allows us to collect data faster than we assess it, researchers are always looking for ways to enhance and accelerate the process between data collection and analysis. We here at Snapshot Safari are proud to have been the first camera trapping project partnering with citizen scientists on Zooniverse when we introduced Snapshot Serengeti, and to have expanded that model from one African park to dozens. Now we hope to improve the data pipeline once again by integrating machine learning to reduce the amount of volunteer effort required to classify data from our participating sites.
‘Machine learning’ refers to Artificial Intelligence algorithms that have been trained for a specific task or purpose. These algorithms are fed millions of images labeled with their correct names and are ‘trained’ to recognize those animals again in different settings. These models generate ‘predictions’ based on the training they’ve received and provide confidence levels to let us know how sure they are that is the correct label. Because Snapshot Serengeti has been running since 2010, it has generated millions of images over the years, which make a perfect training dataset for machine learning (ML) algorithms. We are employing ML models to drastically reduce the effort required to retire empty images (no animals present) and to retire images of common animals like wildebeest and zebra.
First, our ML models have become quite good at telling us whether animals are present or not. This helps us to more easily spot cameras where vegetation has grown in front of the lens, resulting in hundreds of pictures of grass blowing in the wind. Pretty, but not quite what we’re after, so we can eliminate those prior to upload. Secondly, we have modified the retirement rules on Snapshot projects (implemented starting today as new seasons are launched) so that only two volunteers need to confirm the computer’s prediction of ‘empty’. This means instead of 10 or even 20 people viewing those photos, only two people will see them and can push them out of the dataset quickly.
Those of you who have been working on this project for a while know that the wildlife you’re most likely to see are zebras and wildebeest, and you all are great at identifying those! Because those are easy identifications, they too will retire with fewer views than before. What this means practically is that you should see more images of rare and cryptic species like predators and fewer blank images. We have implemented a number of retirement rules behind the scenes to make this happen, based on varying confidence levels produced by the algorithm. For example, our simulations have proven that even at only 50% confidence, the computer is right 99.6% of the time when it tells us that an image is empty. Therefore, any ‘empty’ prediction with confidence of 50% or more will only need two human views to confirm that the computer is correct. Likewise, if the model tells us that it’s a human with a confidence level of 80% or higher, we will retire with just two confirmations.
We will continue to improve the algorithm’s capabilities by using our most valuable asset—all of you! We hope that you will be as interested as we are in advancing the use of ML to make the classifying process more fun and satisfying. The algorithm is pretty good at species, but now we need to improve its ability to count animals, so we will soon be introducing a special project, ‘Snapshot Focus’, which will feature images the algorithm has reviewed and marked each animal with a bounding box. We will ask you to tell us whether the ML model got it right. Stay tuned for that and other special projects!
We are launching three new sites today—Camdeboo National Park, Kgalagadi Transfrontier Park, and DeHoop Nature Reserve, all from South Africa. These three projects have the new retirement rules in place, as will Season 12 of Snapshot Serengeti, which will launch in June. As new seasons or new projects come online, they will be set up with these rules and perhaps more as we refine the data pipeline. Let us and the moderators know how it goes. We are so thankful for your efforts and support, which help us to return data to our collaborators at reserves in Africa quickly and with confidence that it is correct thanks to the combination of citizen science and machine learning. Happy classifying!
-Sarah Huebner
Research Manager, Snapshot Safari
May 28, 2019
For more information about the machine learning algorithms created using Snapshot Serengeti images, see:
Willi, Marco, Pitman, Ross Tyzack, Cardoso, Annabelle W., Locke, Christina, Swanson, Alexandra, Boyer, Amy, Veldthuis, Marten, and Fortson, Lucy. (2019) Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution 10(1):80-91.
Norouzzadeh, Mohammad Sadegh, Nguyen, Anh, Kosmala, Margaret, Swanson, Alexandra, Palmer, Meredith S., Packer, Craig, and Clune, Jeff. (2017) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences 115(25):E5716-E5725.
To read about how algorithms make decisions in comparison to humans, see:
Miao, Z., Gaynor, K.M., Wang, J., Liu, Z., Muellerklein, O., Norouzzadeh, M.S., McInturff, A., Bowie, R.C., Nathon, R., Stella, X.Y. and Getz, W.M. (2018) A comparison of visual features used by humans and machines to classify wildlife. bioRxiv, p.450189.
Ostrich
Whilst we are waiting for the next season of Snapshot Serengeti images I have been reviewing some of the amazing images that season 11 turned up. I always have to remind myself that these cameras do not have some avid photographer sat behind them snapping away at the opportune moment but are activated merely by sensing a change in heat within their detection zone. It is truly amazing how often we get stunning images.
Here a beautiful male ostrich struts across the field of view showing off his amazingly pink legs. The bare parts of male ostrich are usually a pale grey to pink colour but during the breeding season hormones influence the pigmentation and a flush of red blazes through his neck and legs. Given the extent of these legs and neck contrasted with the bold black and white feathers it makes for an arresting sight. Compared with the drab browny grey of the female the male is a real show off.
What’s strange about this scenario is that in most bird species where the male changes feather of bare parts colour it is the female alone that rears the chicks. The colourful male would perhaps attract too much predator attention around the vulnerable nest or chicks. In ostrich though, the male takes his share of sitting on eggs and looking after chicks. In fact he and his primary female will take turns incubating a clutch of eggs that typically include both hers and other females eggs so he will generally have more invested in the chicks as a direct parent than the female who may only be ‘aunt’ to some of the chicks.
I guess the shear size, power and speed of the ostrich, who is perfectly adapted to the open plains of the Serengeti, means he can afford the fancy show of pink legs if it means winning the ladies.
Wondering About African Wild Dogs
African Wild Dog Photo Credit Pexels from Pixabay
The Serengeti is renowned for being one of the few relatively intact large ecosystems remaining in the world. Sure, it isn’t without its problems, nowhere, not even protected areas (PA’s), are exempt from the onslaught of effects from humans either directly on the ground or through climate change but in general the health of the Serengeti is robust.
So why is it in this well balanced, large ecosystem that we don’t ever see African wild dogs (Lycaon pictus) on the Snapshot Serengeti cameras? Of course, African wild dog numbers everywhere are low even within (PA’s) but they are part of the carnivore guild alongside lion, leopard, hyena, cheetah in most of the big PA’s like South Africa’s Kruger National Park and Botswana’s Okavango Delta. So why not the Serengeti National Park (SNP)?
Not so long ago wild dog were present in the SNP in small numbers. In 1970, when studies began, there were an estimated 95 individuals in 12 packs. They were studied sporadically until 1991 when all 12 packs had seemingly died or disappeared. Here in lays the mystery, what killed them?
At the time the rapid disappearance of the wild dogs coincided with a renewed period of research that saw individuals from several packs immobilised and fitted with radio collars. The research community at the time were baffled and a hypothesis was proposed by Roger Burrows that implemented researchers handling of the dogs as being causal to their decline, the theory being that the stress imparted to the animals made them susceptible to rabies which eventually killed them. It is a controversial hypothesis and has had the research community at each other’s throats for the last 25 years. Some argue that invasive handling of study animals is un-ethical and can lead to tragic outcomes (as hypothesised by Burrows for the African wild dog) others argue that collaring and taking blood samples from study animals is vital to understanding processes which effect conservation management.
A recent paper by Jackson et al, “No evidence of handling‐induced mortality in Serengeti’s African wild dog population” an open access paper published in Ecology and Evolution, revisits the question and aims to shed new light on the argument with research.
I am not trying to weigh in on the argument, my expertise is inadequate for that but I thought it was an interesting take on the question of wild dog in the Serengeti and it used data generated by Snapshot Serengeti, all be it in a small way, to help with its argument.
Surrounding the eastern side of the SNP are two PA’s, the Ngorongoro Conservation Area (NCA, a multi use area occupied by a large population of Maasai pastoralists) and Loliondo Game Controlled Area (LGCA, a multi use area with some settlements, hunting and tourist concessions.) One would argue that these areas are not as pristine as SNP itself with a good dose of human impact but the wild dogs have been studied here since 2005 and as of 2017 there was an estimated population of 120 individuals in 10 packs.
Collaring has shown that the wild dogs do venture back to the Serengeti plains from time to time (proving there is no physical barrier to dispersal) but that they do not settle there, choosing instead to stay in an environment where one would imagine it was harder to survive on the periphery of human habitation. Our own Snapshot Serengeti work comes in to play here to prove that even with an extensive network of cameras that have been in operation for several years no wild dog has been recorded in SNP.
So what do Jackson et al imagine could be the route cause if not the direct handling leading to stress related rabies outbreaks hypothesis.
The team have studied wild dogs for over a decade in areas adjacent to SNP that arguably have an equal or higher rabies risks (think of the domestic dogs associated with people) to the SNP. They have used the same invasive methods of study as the SNP researchers including using intervention to fit collars, take blood and in one incidence an attempt at relocation back to SNP. They believe that they have the perfect scenario in which to test the hypothesis.
They found, in contrast to the earlier study which saw the entire population disappear, that 12 month survival rate in handled wild dogs was 87.6% and in a group of 67 wild dogs captured and translocated back to SNP, held for almost a year in translocation enclosures, a long term stress situation, 95.5% survived over 12 months. Incidentally, most of the relocated dogs did not stay in the SNP, returning to the adjoining PA’s. One pack did remain but not in the former study area of grassland plains but rather in a rugged area just outside the original study area.
The team argues that the wild dogs in both NCA and LGCA have been subject to handling just as much as the original study yet have shown a population increase, secondly, there has been no repopulation of the original study area either naturally through dispersal nor through attempted reintroduction something that arguably should have happened if the only reason for the die off was human induced.
Instead they believe that the demise of the wild dog coincided with a rise in lion and hyena numbers on the plains of the Serengeti. Wild dog are killed by these predators but perhaps more importantly they also have their hard earned prey stolen from them by larger predators. Their theory is that the competition from increasing lion and hyena numbers as well as out breaks of rabies and canine distemper saw the death of some packs and the dispersal into the adjoining PA’s of the rest. Compared with the endless grassy plains of the Serengeti, the NCA and LGCA are much more varied terrain with a mixture of hilly, rocky areas as well as open woodland and open grassland. This kind of mosaic gives wild dogs a much better chance of avoiding larger more dominant predators and so their chance of survival is greater.
So could this be why we see no wild dog in our camera traps. Whatever the reason it highlights that even with what we imagine as well protected areas the space we have left for wildlife is minimal and to protect a wide range of biodiversity we should be doing more to protect a wide range of ecosystems and habitats.
If you want to read the Jackson paper you can find it here: https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.4798
There are links within the paper to the original Burrows work so you can get a feel for both sides of the story.
A Picture Worth A Thousand Words – Part 2
I have written about this before, I know but this series of images has got me going again.
The Snapshot Serengeti images are great and they have captured some stunning stuff over the years (melanistic serval, oxpeckers roosting at night on giraffe, a buffalo hunt by lions) and individually they have produced some amazing portraits but every once in a while, the old adage ‘a picture is worth a thousand words’ doesn’t quite ring true.
I posted images of a male seemingly strolling through the savannah a while back, musing on what he had been up to. The other day whilst browsing through images I found more parts to the picture. More images of him moving at different times and a female.
I am sure that Snapshot Serengeti followers could add to this series of images if we delve further but I was intrigued.
What’s happening? Is it a male and female spending some days together whilst mating (as happens with lions)? Or are there other pride members around? The female looks as though she has blood staining her face so perhaps, they have been feasting on a kill, alternately moving back and forth to water or shade between snacking.
It is one of those instances when you would like the camera to just swivel a bit to see if we can learn more but technology (or at least affordable tech) has not quite reached that state yet.
So, we will just have to sit back and enjoy what we can see, a pair of very full looking handsome lions and let our imaginations do the rest. Sometimes no knowing is part of the fun.
Cheetah Tactics?
Here at Snapshot Serengeti we are lucky to get regular good images of all the big cats. Lions feature the most frequently followed by cheetah with leopard being the rarest. This is not surprising when you think that lions like to spend the day sleeping and resting after their night-time prowling. This means they make a bee-line for the shade of trees. Cheetah have the same idea, staying out of the sun during the day. Leopard of course tend to be up those trees and so its harder to capture them on camera trap. We are still waiting for the day that we get a capture of a fury belly as a leopard leaps up over the camera on its upward trajectory. Imagine how hard that could be to identify!
Looking through some of the recent images from the most recent batch we came across this series of three images of a cheetah walking right up to the tree the camera-trap was on, presumably to settle down for a nice nap, but is that all there is to this tree/cat relationship?
Cheetah, as I am sure you are aware are at the bottom of the big cat chain. They don’t do well in a fight against lion, leopard or hyena for that matter so they have to stay alert to danger. They need time to slink away or flee at speed.
So now picture this, a cheetah is sleeping in the grass, it wakes up and wants to survey the plains to see if there is either prey or danger about. It sits up and bang! its seen by anything that happens to be near by. Now rewind a bit, the same cheetah has a nap under a tree. This time it sits up to survey and its slender body is masked by the trunk of the tree. Its not nearly so easy to spot from a distance.
Ok so its just a theory and probably cheetah primarily spend time underneath trees from a purely physiological perspective but it would be nice to think they were also using their tree as a point of tactical surveillance. Certainly in the hot shimmering air of the Serengeti it is hard to see a cheetah that is sitting upright under a tree until you are pretty close by. In my experience although lions like a tree too its just as likely that you will find them lounging in the shade of an erosion channel or small bush but cheetah always seem to by in that classic pose under a sturdy tree when resting.
It gets you thinking!
Machine Learning Update
Hartebeest through a dislodged camera-trap cover.
It has been just over a year now since Snapshot Safari was launched. Snapshot Serengeti as the original Zooniverse citizen science camera trap project has remained the flagship project but there are now several other projects under the Safari banner.
One of the good things about joining forces with all these other projects is that collaboration tends to bring perks that operating on your own doesn’t. For example, different areas of expertise that some of us would just never have thought of, being somewhat far from the mind of field researchers and ecologists.
I am talking about the computer learning side of things, ML (Machine Learning) in particular. The team behind Snapshot Safari have been working hard on this aspect essentially to help speed up the rate of classification needed now that there is so much data across different projects.
They have recently engaged a specialist in machine learning at the University of Minnesota who is helping them to develop ML for use with the Snapshot Safari projects.
The idea is to run an algorithm on the data prior to uploading it to the Zooniverse, this will identify most of the misfire and vegetation only images meaning us volunteers will be left with more of the good stuff, the actual animal images.
The algorithm has been trained on millions of images from Snapshot Serengeti, now for these latest batches the team have asked the ‘machine’ to predict which species it is seeing, how many and what the behaviour of the animals is. Don’t panic we really are in the early stages and the idea is to compare what it came up with against the actual results from the volunteers whom we already know are pretty darn accurate. The team doesn’t expect great things yet from the ML and doesn’t foresee computers taking over any time soon but it will be interesting to see how the ML goes.
So, for now things will be continuing as normal, no changes in how you classify or what you do but hopefully there will be fewer blank images with no animals. As the team stresses, they are a looonnng way from machines taking over.
For those of you who enjoy being part of this developmental side of the project the Safari team will soon be launching a side project called Snapshot Focus. It is designed to also look at how well the ML is at recognising animals in trickier images, especially those with multiple animals. If you feel like helping out as a break from the usual work flow it couldn’t be simpler. All you have to do is answer yes/no as to whether the computer has managed to put a bounding box around every animal in the image.
Look out for updates from the team over the coming months to learn more about these developments but meanwhile there are still lots of images to classify on Snapshot Serengeti.
Merry Christmas from the Snapshot Serengeti Team
The last month has seen a new batch of Snapshot Serengeti Images uploaded to the Zooniverse. There seems to be a high portion of really stunning images amongst the 40000 odd that were uploaded in this batch. Now there is no rhyme nor reason for this as the camera-traps are impartial, they simply snap away when an animal triggers them. It is pure luck if the resultant image is a perfect portrait or a tip of a horn or tail.
Of course, as scientists we don’t so much care about pretty pictures as being able at least to id the animals involved but no one is immune to a great image and so we at Snapshot Serengeti give a great big thanks to the animals of the Serengeti for being so cooperative when it comes to our camera-traps.
As you classify the images do remember to flag any really special ones for everyone to enjoy.
Christmas is approaching and so from all the team at Snapshot Serengeti Merry Christmas. Its been a big year for us moving over to the new system and joining up with Snapshot Safari, thanks for sticking with us through the teething problems and a big thank you for all your classifications over 2018.
A Team Effort
Snapshot Serengeti has been on the go since 2010 in one form or another and over those years a team of dedicated people has kept it running. The base of the effort is the 225 camera-traps that have been snapping away continuously for that whole period. Of course for that to happen there needs to be researchers and assistants on the ground physically looking after camera-traps, a scientific team who coordinate all data processing and analysis, a management team running the administration of the project and generous funders to keep everything alive and kicking.
Snapshot Serengeti could also not work without all the thousands of volunteer citizen scientist who generously give their time and energy to classifying all the millions of images, ultimately helping the researchers to answer scientific questions we hope will aid in the conservation of all that we love about the Serengeti.
Here in these blogs we have celebrated all these people but it dawned on me recently that there is one group of people that seem to have been forgotten, our moderators.
Our amazing team at Snapshot Serengeti deserve a special mention. They, like our citizen scientists are volunteers, dedicating their time and expertise for free. Contrary to what some may think they are not part of the scientific team in as far as they are not university students who do the job as part of their studies. No, they are a mixed bunch in terms of back ground and do the job plain and simply because they love the Serengeti and love the project. They spend a huge amount of time online helping other users with their classifications, guiding new users through some of the pit falls they know only too well and sharing their collective knowledge through prompt responses to questions and great information posts helping others with less experience to understand the Serengeti and its wildlife. They also have to deal with the odd, luckily very infrequent, troll which is a thankless task in diplomacy. We are privileged to have such an amazing team and I know that they are greatly appreciated by Snapshot Serengeti’s participants.
So thank you to davidbygott, maricksu and tillydad who have been with us since the beginning and welcome to parsfan and nmw. You Guy’s are the best and Snapshot Serengeti would not be the experience it is without all your help.
Hartebeest through a dislodged camera-trap cover.
Vulture Culture
Lappet-faced vulture
Of all the large birds out there on the Serengeti plains the vultures are probably the most recognisable, with their long barely feathered necks and large hooked beaks, you can’t miss them. For a lot of people they are ugly birds and their behaviour makes people shudder, all that frantic plunging of necks inside messy carcasses. Some cultures revere the vulture, instilling magical attributes to them whilst others vilify them.
But the truth is they are just birds with their own unique ecological niche, one that is absolutely essential to the health of the whole ecosystem.
Lets break it down. That neck, well the fact that the whole head and neck is either bare or only lightly feathered is a marvellous adaptation to keeping clean. Yep, not something people generally associate with vultures but they are in fact pretty clean birds. That beak, well it may look like it could kill quite efficiently but it is actually designed to grip hard and rip. Believe me I have been around rescued vultures and felt the effect, there is a lot of power there. A vulture will use its sizable feet to hold a tricky bit of carcase down when trying to tear off a chunk but the real power comes from the strong neck muscles and the powerful beak.
If you look closely at the different species of vulture you will notice that not only is there a difference in size of individual species but in the beak size. There is a hierarchy in vulture etiquette at a fresh carcase. If the dead animal is large and has a tough hide, say a buffalo, the larger species such as lappet-faced will be needed to ‘break in’. Their huge beaks and added bulk allow them to head straight for the good bits where lesser vultures would have to start with the natural openings such as the eyes, mouth and anus. Once the large vultures have broken in, the rabble takes over and fights it out for the good meat, the white-backed and Ruppell’s. Around the peripheries the hooded vultures will be waiting for the chance to snatch up bits of anatomy that are sent flying by over- zealous cousins or to dart in when the carcase looks almost clean to pick off the last bits of flesh in hard to reach places. It is not uncommon to see this species right inside the empty carcase and its slim lined beak is great at cleaning up.
Vultures search for food from the wing. Research has shown that in the Serengeti it is most often the lappet-faced that arrives first despite the numbers of this species being lower than the others. It seems they are extra vigilant or just have better eyes. The descent of this the largest African vulture draws in the other species who can be clued into the find from over 30kms away. It is quite breath taking to see many vultures on rapid descent with wings held inwards, feathers splayed it can also be noisy.
But other than cleaning up unsightly dead things how do vultures help the ecosystem? Like other organisms that consume dead animal matter vultures are immune to a lot of deadly diseases. Their stomachs are filled with very acidic digestive juices which can destroy diseases such as anthrax, cholera and rabies. Most scavengers would not be immune to these types of disease and what’s more, diseases such as anthrax can lay dormant for decades posing an ongoing risk. Of course vultures alone can’t keep the Serengeti disease free but with their ability to fly over 100km a day they do a darn good job of patrolling the plains and keeping them clean.
But outside of protected areas vultures are in decline. In places like South Africa there has always been a value placed on vultures with Sangoma or witchdoctors prescribing vulture heads for people needing to see into the future to answer big life questions. Of course this has modernised, now people purchase vulture heads to see the winning lottery numbers. Vultures are also targeted by poaching gangs who, in an effort not to have their poaching camps discovered, place poison bait to attract and kill the vultures. India, several years ago nearly lost ALL its Gyps vultures. 95% where thought to have died and the main cause discovered to be adverse effects from a drug called diclofenac that was widely given to domestic stock. The drug has since been banned in India but its use as a veterinary drug in Africa is rising causing major concern amongst conservationists. Loss of habitat is also an issue.
We are lucky that the Serengeti is an ecosystem functioning normally with all its facets. You may be lucky to spot, lappet-faced, white-headed, white-backed, Ruppell’s griffon, hooded and Egyptian vultures in our camera trap images. It is quite remarkable to find this type of balance these days and we thank the vultures for their ongoing services.
Snapshot Serengeti 