Snapshot Serengeti is in the limelight again!
A new paper titled “No respect for apex carnivores: Distribution and activity patterns of honey badgers in the Serengeti” has been published by a team from the University of Wisconsin and University of Ljubljana using the Snapshot Serengeti data classified by our citizen scientists.
Honey badgers are surprisingly understudied. Although extremely charismatic the fact that they have large territories, up to 541km2 for an adult male in the Kalahari, and no clear habitat preferences makes it hard to predict where to find and study them.
The Snapshot Serengeti data of course is a dream come true to many researchers enabling them to ask scientific questions without having to wait potentially years to collect data themselves.The team took advantage of the open access data, courtesy of Snapshot Serengeti to look at what they could learn about honey badgers and how they live alongside other predators. Ferocious as they are honey badgers are killed by lion, hyena and leopard and so the team wanted to know whether they avoided areas where these large carnivores were active.
Well it seems that despite ending up as an occasional victim the honey badger is quite happy living alongside the larger carnivores, at least in the Serengeti anyway according to the authors. It appears as if the honey badger actively seeks out the same habitats as the large carnivores. The authors modelled a variety of different explanatory scenarios to see which would be the best fit to explain honey badger distribution across the Serengeti study area. Included where variables such as habitat preference, water availability, cover availability, lion abundance, and leopard abundance. Their best models showed that the presence of all three large carnivores coincided with the presence of honey badgers and that there was also a positive correlation temporally between leopard, hyena and honey badger showing that they use the same habitat at the same time.
It’s interesting stuff. The authors do point out that although the data set was huge there was actually very few incidence of honey badger over the 3 year period covered by their work and so their sample size was small. It does however show just how valuable the data collected by Snapshot Serengeti and the other Snapshot Safari projects can be, if nothing else to give scientists a relatively inexpensive way to explore questions before undertaking more specific research work themselves.
You can read the paper here, although it is not open access unfortunately: https://www.sciencedirect.com/science/article/pii/S1616504717302720
These two images illustrate the point nicely, you can clearly see the same camera has captured honey badger and spotted hyena with in 13 days of each other. Interestingly both in day light.
Whilst stretching the corners of my brain to think about a new topic to write about in the Snapshot Serengeti blogs it astounds me to realise just how long we have been going for; over 7 years now as Snapshot Serengeti and almost 10 if you include the Serengetilive days.
It is also humbling to know how dedicated our followers are and what support we get from them. Our fun would have been over long ago if the community had not backed us. It has occurred to me that Snapshot Serengeti’s followers do so in differing ways. Those who follow our facebook and twitter pages or WordPress fans who follow us through our blogs may have missed what it is we are up to. So at risk of boring those of you who do know I thought it was about time to reiterate what it is we at Snapshot Serengeti do and how it all works.
Our largest group of followers do so at www.snapshotserengeti.org helping us classify the millions of camera-trap images that are produced by around 225 camera-traps placed in a permanent grid pattern in our study zone in the Serengeti National Park. For continuity’s sake these sites, after an initial bit of trial and error have remained in their fixed spots since they were first chosen by the projects designer, Dr Ali Swanson back in 2010.
Originally the camera-trap grid was set up to answer questions on carnivore interactions specifically if carnivores were avoiding one another spatially and temporally, it soon became apparent that it could be used to pose many more scientific questions amongst them herbivore coexistence and predator prey relationships. The wisdom to leave this permanent window of observance into the lives of the Serengeti animals should lead to many future studies and has spawned many new similar camera-trap projects around the world.
It’s not all about the animals, in fact since teaming with Zooniverse the project has been as much about the advancement of citizen science as anything else. Back in the Serengetilive days there were so few of us taking part that we used to have our names up in a sort of league table of who had classified the most images. Each classified image was labelled by the classifiers name. Now of course there are far too many participants to bother with that kind of thing, besides with multiple people having to agree on each classification it might get messy. The work on developing a robust algorithm that dealt with the uncertainties in each individual classification was so involved it also paved the way for many more projects and several scientific papers.
So what do we ask classifiers to do? Well first you are presented with either a run of 3 images (day time) or 1 image (night time). You are then asked to decide and record what animals are present, numbers of each species, behaviour and whether there are young present or not. It’s pretty straight forward with prompts along the way. If you don’t know what the animal is you simply guess. Yes you read that right, you guess. One thing the developers worked out is that the whole project works better if you cannot skip images. For one thing it avoids all the hard or boring images being left till the end. As each image has to be agreed upon by several classifiers before it is retired this tends to smooth out any miss classifications and research has shown we are around 97% accurate.
If you find something good or something you cannot id and are curious you can add the image to Talk which is the discussion forum. There we have some very dedicated moderators who will help you with your queries.
All in all Snapshot Serengeti is about learning and sharing both for the researchers and for the community of classifiers so if you have been enjoying the facebook posts or reading the blogs but have never had a go classifying get yourself over there to www.snapshotserengeti.org and have a go.
I promised I would have some news about what the Serengeti team has been up to recently in the field. Our beloved camera-trap grid is still being cared for, cards downloaded, batteries replaced and cameras given the once over. So all is well on that front but what is the latest question being asked by the team.
Well thanks to the spatial occupancy modelling of the Snapshot Serengeti camera-trap grid we have learned a lot about how the animals share the environment. What we can’t derive from the camera trap images is the details of what the different species are doing when they are in those spaces and how so many large herbivores can exist together. It could be that they simply facilitate each others foraging or maybe they are using different resources. Scientists have identified what is known as niche partitioning, a mechanism that sees different species specialising in eating different proportions of grasses verses non-grasses; pure grazers and pure browsers and a sliding scale between the two. A second mechanism sees different species eating different parts of the same plant.
These two mechanisms seem to make perfect sense but it is not understood to what extent these two truly affect coexistence of large herbivores. This is where the Snapshot Serengeti team research comes in.
Under our own Dr Michael Anderson they have teamed up with Dr Rob Pringle and researchers at Princeton University in using a revolutionary new analysis method known as DNA metabarcoding to see what exactly each animal is eating.
Up until recently scientists studying herbivore diet had two choices, they could watch their subjects and try to identify what they were eating or they could use microhistology, whereby plant parts in faeces are visually identified. As you can imagine these methods are fine for differentiating between, say, grasses and trees but don’t allow scientists to classify down to individual plant species. With DNA metabarcoding they now have that ability and it should tell us a whole lot more about how the animals divide their resources in space and time.
So that’s the science but how does the team collect this data. Well as with microhistology it involves dung. Our intrepid scientists are roaming the Serengeti collecting poop from as many different herbivores as they can and then it all has to be shipped back to the labs for analysis.
If you are thinking that our team must be highly skilled detectives able to identify a wide variety of brown pellets in the savannah grasses then think again. That’s not to say they can’t but this work relies on 100% knowing which species produced said dung its sex and age as well as a sample that has not been contaminated in anyway. The method of collection relies then, on stealthy observation waiting for an individual to lift its tail and sprinkle the ground with brown pellets before running in with your sample jar at the ready to collect the freshly deposited “clean” offerings. I have some experience with this work and believe me it does feel slightly odd to be observing animals in this way, willing them on to have a bowel movement so you can move on to the next species. It is also a little risky as you can get so engrossed at watching your target animal that you forget there are predators there watching and waiting. At least in this project it is only herbivores the team are interested in, to do the same with predator’s faeces, that’s a whole lot more smelly.
The study is still in its early stages but the team reports they are already seeing some noteworthy things.
Spoiler alert, early results suggest that there are only two ‘pure’ grazers in Serengeti (zebra and warthog) and lots of variation between wet and dry season.
We will bring you further updates once the team has finished their analysis work and have the full results. It promises to be exciting stuff. In the meantime you can think on the glamorous job a field scientist has whilst you stay clean at home helping with the job of classification.
Symbiotic relationships are common in the Serengeti. They fall into two main types, mutualism, whereby both partners benefit from one another and commensalism, whereby one partner benefits from the actions of the other but the other partner is largely unaffected or unharmed. I wrote recently of oxpeckers and large herbivores, large herbivores provide food in the form of ticks for the oxpeckers and oxpeckers provide a cleaning service for the large herbivores, a good example of mutualism. Birds such as cattle egrets that follow buffalo around to catch the invertebrates the buffalo disturb as they graze is an example of commensalism. Of course it is not just animals that have symbiotic relationships; my blog last week on termites and mushrooms was another example of mutualism.
So what about zebras and wildebeests? We see them all the time on Snapshot Serengeti in mixed herds, grazing peaceably with one another. Is this just coincidence or is this a form of symbiosis?
It is actually hard to say and of course that is why labelling things, especially behaviour is often tricky.
Zebra and wildebeest are both grazers meaning they mostly eat grasses but that doesn’t mean they share the same diet. They preferentially eat different parts of the plants that they consume. Zebras are quite content chewing longer tougher grasses where as wildebeest prefer shorter, more tender shoots. This partition of resources means they can quite happily graze side by side with out exerting pressure on each other.
Another good reason to team up is the extra safety that numbers provide. Not only do more ears and eyes provide better early warning systems but the odds of the individual being targeted by a predator are reduced when there are greater numbers to choose from. Apparently zebra have better eyesight but wildebeest have better hearing so the two complement each other.
There could be another reason. Our very own Meredith Palmer just published a paper about interspecies reaction to each other’s alarm calls, you can read it here: https://www.sciencedirect.com/science/article/pii/S0003347217304207
She found that zebra, wildebeest and impala recognise each other’s alarm calls but that they did not always respond in the same manner. When zebra sounded the alarm all three herbivores reacted strongly but when impala gave the alarm zebra where likely to ignore it, or assess the relative danger themselves. It seems that this varied response is down to predator size. Impala are prey to a wide range of smaller predators that would not be able to handle a mammal the size of a zebra, so when impala give the call it doesn’t always signal danger for the zebra. However when a zebra, the largest of the three herbivores sounds the alarm, whatever it has seen will probably be able to take down the wildebeest or the impala too so it’s prudent that all three scarper.
It is an interesting reaction and maybe wildebeest hang out with zebra because they are more trustworthy alarmists. I am not sure that the companionship of zebra and wildebeest can be classed as symbiotic I think it is more of an interaction due to a shared habitat but it seems that on some level they can benefit each other.
You are probably aware that the 225 camera traps of Snapshot Serengeti are set out in a grid pattern, spaced every 1km over a part of the Serengeti National Park. It sounds relatively simple but actually there is a lot of painstaking scientific pondering as to how exactly to set out your camera traps.
Over the last couple of decades there has been much debate as to the best way to design a camera trap study. The main choice, in terms of placement pattern, is whether to place your camera traps randomly or selectively and what kind of spacing/density to use.
Truly random is to grid your study site and then let a computer randomly choose which grid squares to place the cameras. Alternately you can choose a line or grid and place your camera trap at regular intervals regardless of where that may fall, still a random point. With selective placement each site is carefully chosen for a specific feature.
In reality most projects use a mixture of the above methods and the best method is really determined by what your scientific question is. For instance, if you where trying to acess the number of leopards in a given area it is better to place your camera traps strategically in places you know or guess leopards are most likely to pass rather than using a randomised method. However if you are carrying out a census of an area and wish to know what species are present then a randomised grid is ideal.
As I said a mix of methods is often used. Imagine setting out a grid in the comfort of your office on your computer. It looks good, covers a large area and promises good results. Once out in the field you navigate to your carefully worked out GPS reference point only to discover it is slap bang in the middle of a marsh or in a thick overgrown patch of thorn trees. This is where the scientists allow themselves a little leeway. Often they will take the GPS point as home base but choose an ideal spot within a certain radius of this point where perhaps there is a game trail or some other sign of animals passing, thus allowing them to select a good site within the vicinity.
I have recently had experience of this type of placement and I can say the work done in selecting your study site and then laying out your grid onto a map is laborious but not nearly as much as stomping through the bush keeping your fingers crossed that your next randomly selected site will be perfect. Turning up to emplacement three to find a thick tangle of vegetation is a little soul destroying, mostly you wonder if any animal is likely to bother to pass that way. The reality is that you normally find a spot that is better within 10 meters and with some slight pruning of the vegetation the sites can often turn out remarkably productive.
So that is the placement sorted but there is a long list of other agonising variables to consider, what settings to use on the camera trap itself, how many to use and how long to keep them up. Believe me every scientist designing studies deliberates the pros and cons of these factors and worries incessantly about if they have made the right choice. You don’t want to set up all you camera traps and leave them for a few months only to find your set up was not great, something which happened to me recently when I chose to set the camera trap on high sensitivity to make sure I had every chance of capturing the small, fast critters. The problem was it was so hot, 40°c plus, that the ambient waves of heat set the camera trap off almost permanently between 12pm and 5pm leaving me with 2000 images of nothing. I have had to compromise and reduce the sensitivity to avoid all the miss triggers; hopefully it won’t miss too many small things.
Snapshots camera traps have now been up for over 7 years so most of these teething problems have been ironed out. But as with the best laid plan you cannot control everything, the odd camera still malfunctions as I am sure that our regular classifiers can attest to!
*This weeks blog was written by Jamee Snyder, project coordinator and administrative assistant with the Lion Lab, University of Minnesota. She tells us all about a wider a project that Snapshot Serengeti has evolved into and what we can look forward to in the near future.*
Seven years ago, the University of Minnesota Lion Center set out 225 cameras in Tanzania’s Serengeti National Park. These cameras have recorded over 50 species including some of the most threatened species on Earth. With help from over 140,000 citizen scientists from around the world, millions of photographs were reviewed and classified over the past seven years, which provided park managers, conservationists, and researchers with the necessary information to analyze African wildlife population dynamics. This collective effort is a major contribution to ecological research, allowing for the evaluation of long term trends in wildlife populations as well as best practices in conservation management of charismatic african mammals.
Snapshot Serengeti was one of the first camera trap surveys to document wildlife populations in a national park and is now one of the longest running camera trap surveys in the world. We have learned a lot over the years, from how to keep our cameras safe from hyena jowls to retrieving data from memory cards that have gone through a wildfire. We are continuously looking for ways to improve this project.
Thanks to years of experience, your participation, and help from several organizations in the U.S. and Africa, we are excited to announce that Snapshot Serengeti is expanding into an international conservation initiative called, “SnapshotSafari.”
Don’t worry! Snapshot Serengeti isn’t going anywhere. In fact, it will remain essentially the same as we transition into our new platform. The discussion forums and personal image collections will still be available to current and future users. Now, participants will be able to see numerous other parks in addition to the Serengeti. SnapshotSafari will showcase camera trap images from multiple camera trap grids inside dozens of parks and reserves located in six African countries. Intrepid citizen scientists will be able to choose from various exotic habitats, including but not limited to: the Sand Forests of KwaZulu-Natal, the Lowveld of Limpopo, the Fynbos of South Africa’s Cape, and the Karoo desert, in addition to such remarkable ecosystems as Mozambique’s Niassa Reserve, Tanzania’s Ruaha National Park, Swaziland’s Mbuluzi Game Reserve, and Botswana’s Makgadikgadi Pans National Park.
By incorporating multiple sites, we can ask more complex questions regarding African wildlife populations and the factors that contribute to ecosystem stability. For example, researchers can compare population dynamics of reserves that are fenced versus those that are unfenced, or theycan evaluate the environments that successfully host multiple predator species without depleting prey populations. Researchers at the Lion Center will use this dynamic platform to investigate the cascading effects of large mammal reintroductions and ways to limit direct human interventions while still maintaining stable ecosystems within fenced reserves. SnapshotSafari provides an opportunity for participating reserves to collaborate and subsequently develop the most effective conservation strategies for protecting biodiversity.
We are working hard to get SnapshotSafari ready to launch in January. We just completed beta-testing, and the feedback has been very positive. To all of the citizen scientists who participated and to those who continue to be involved with Snapshot Serengeti, we are extremely grateful!
Now, we need your help to finish classifying the final series of images on our original platform, Season 10, at http://www.snapshotserengeti.org before we initiate SnapshotSafari, which will host Season 11. We are very close to finishing classification of these images, so don’t hesitate to invite your friends and family to take a trip to the Serengeti through the lens of one of our camera traps and classify wildlife. Let’s push this meter to the end!
Stay tuned for an official count down, so you can be one of the first to participate in SnapshotSafari and contribute to our collective knowledge and ability to successfully conserve African wildlife.
Snapshot Serengeti has around 225 camera-traps laid out in a grid in the heart of the Serengeti National Park. They have been there for around 7 years and make up one of the longest running camera-trap monitoring projects in the world. Snapshot was launched on the Zooniverse portal in December 2012 and has inspired many more similar camera-trap projects from around the world. So Happy 5th Birthday to us, may there be many more to come.
There is no doubt that camera-trapping has gripped the hearts and imagination of both scientists and the public. Eight years ago when I first used camera-traps I had to explain them very carefully to friends and family as they had never encountered them, these days references to camera-traps appear in popular press articles and wildlife documentaries and most people have a basic idea of their use in conservation.
It was K. Ullas Karanth, an Indian wildlife zoologist, who is credited with pioneering the use of camera-traps as scientific tools in his study of tigers in the 1990’s. In the last two decades the technique has advanced at a hugely fast pace and has revolutionised the study of elusive and seemingly well known species alike. It is a scientists dream to observe animals without being present yourself to influence their behaviour.
But looking at the history of the discipline I can across many references to much earlier work using camera-traps. Back in 1927 National Geographic published an article by Frank M Chapman titled delightfully “Who Treads Our Trails”. The piece opens with this amazing paragraph
“If there be any sport in which the joys of anticipation are more prolonged, the pleasures of realisation more enduring, than that of camera trapping in the Tropics I have yet to find it!”
This guy would have loved Snapshot Serengeti. This is most likely the very first scientific paper to report on using camera-traps all be it very different cameras. His rig involved a tripwire the animal steps on rigged up to the camera shutter and bowls of magnesium that will explode and create the flash needed to illuminate the animal at night time. It seems incredible now that this would be allowed considering today’s ethically minded ethos but the author himself points out that the alternatives to studying animals could include using dogs or trappers to catch an animal or even poison bait. He decides he wants a census of the living not a record of the dead and so the idea of camera-traps for scientific study are born. He drew heavily from the work of George Shiras who published the first pictures taken by remote camera back in 1906 (also in National Geographic). George Shiras took the pictures for the pictures sake only later becoming involved with conservation but Frank Chapman was a true scientist.
Obviously the technology has changed a lot and the loud noisy explosions that accompanied Franks work have been replaced by covert black IR where even the glow of the infra-red flash is almost invisible. He would marvel at the amount of pictures that can be stored on an average SD card and that camera-traps are being used from the tropics to the snowfields of Antarctica.
You can look for the original article with this reference:
Chapman, F.M., September 1927. “Who Treads Our Trails?“, National Geographic, 52(3), 331-345
Or visit this site to see some of Frank Chapman’s images: http://www.naturespy.org/2014/03/camera-traps-science/
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.
Of all the antelope that we classify on snapshot Serengeti the eland is one of the most distinct. Its massive size, heavy set horns and swinging dewlap lends it a bovine appearance yet it is an antelope – all be it Africa’s largest. A member of the Tragelaphini family or spiral horned antelope the eland is closely related to kudu, nyala and bushbucks.
There are two species, the common eland (Taurotragus oryx) we are familiar with in the Serengeti and the giant eland (Taurotragus derbianus) found sporadically in woodland savannah across Central and West Africa. One thing to get straight is that giant eland are on average less bulky than their common cousins, the ‘giant’ refers to their horns.
At close to a thousand kilograms in weight a fully grown male eland equipped with a fortified neck and viscous hefty horns could prove a lethal adversary. Perhaps it is unsurprising then that most male interactions are highly ritualised and the real fighting only really occurs between males of near equal stature. It is an unusual trait in male eland that the neck, shoulders and dewlap continue to enlarge as the animal ages. They develop tufts of wiry hair on their foreheads and noses and a strange clicking in their knees develops that is audible quite some distance away. I remember once sitting in a clearing in the bush and hearing what sounded like multiple people cracking their knuckles whilst moving closer towards me. I could not even begin to imagine what was fast approaching me and began to get a little nervous, looking around for a tree to climb. I heard nothing else but the odd branch moving until out from the edge of the bush appeared a small group of eland. Much to my relief it was precisely these knee clicks that I had heard.
Although not a particularly fast running antelope eland are noted to be extreme jumpers. They are capable of leaping over three meters high from a standstill which to me puts to bed any lingering doubts that they are antelope not oxen.
As many of our snapshot images attest to they are often found in quite large herds, congregations 100’s strong are not unheard of. But all the same there is no real structure to the group. Herds can comprise all males, all females or mixed sexes and ages. They are highly interchangeable and very few bonds are formed. Even the sacred mother and calf bond is tenuous in eland society. Calves form crèches when they are a few days old and prefer to hang out away from the adults. They only suckle once or so a day and that can be the only time spent with mum. Female eland will band together in defence of their young but as they are often out of sight of the youngsters this doesn’t happen too often. Instead young eland grow fast attaining 450kg in their first year.
Although life seems good in these juvenile gangs and generally eland are long lived, mortality can be high in youngsters. Whilst studying leopard in South Africa we found eland was a common prey item, in fact we discovered three kills within a month of eland less than six months old and those were just the ones we found. Lion and hyena are also known to take their toll. There is no real synchronised birthing in eland herds with young born at anytime. I guess this means there is always a slightly younger, less savvy, youngster in the crèche that is easy prey for predators.
Next time you find an image of an eland herd have a close look to see if you can work out if they are females (smaller with more slender horns), males or if perhaps it is a crèche.
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