One of the animals that continue to be a rarity on snapshot Serengeti is the rhino. We have had a handful of capture events over the years.
There are two species of rhino found in Africa; the white or square lipped and the black or hook lipped. The Serengeti is home to the latter. It is a large bulky mammal and as such many a hopeful #Rhino has turned out to be a blurry image of an elephant or buffalo. Things can get confusing with some of the images but isn’t that half the challenge trying to guess those indescribable blobs? Surely it wouldn’t be the same if everything was easy to id?
Anyway back to Serengeti rhinos.
Just 50 years ago between 500 and 700 Eastern Black Rhino (Diceros bicornis michaeli) roamed the Serengeti-Mara ecosystem but during the seventies the population was decimated by poaching to around 10 or so individuals.
A huge effort is being made by various conservation and government bodies with enormous donations by private individuals to save the population from total extinction. Notably a remnant population in the park was highly protected and slowly, over the next few decades the population made some recovery. In 2010 it was decided that the Serengeti area was being protected well enough to try and bolster the resident rhinos with new genetic stock. It just so happened that a private owner in South Africa had a breeding herd of Eastern black rhino that had been part of the attempt to safe guard the subspecies back in the 60’s, these animals had originated from Kenya. With strict controls by IUCN officials it was deemed these animals were of the right genetic stock to be reintroduced to the Serengeti.
The plan was to translocate 32 rhinos over the next few years and release them in a new site close enough to allow some overlap with the resident 30 or so rhino. Unfortunately the project has been affected by the recent escalation in rhino poaching and it is difficult to find how many rhino have been successfully translocated to date let alone the current Serengeti total population but you can bet it is still small. The IUCN red data list states Tanzania as having 88 Eastern Black Rhino in 2011.
If you are lucky enough to stumble across a rhino capture on Snapshot Serengeti you should definitely celebrate … and don’t forget to # it.
The Challenges of Field Work
If you have clicked through the seemingly endless captures on Snapshot Serengeti then you must have realized just how many cameras are snapping away out there in the Serengeti. Have you ever wondered who looks after those cameras?
Researchers sometimes go to extreme lengths to collect their data and not much deters them from their goal.
On a recent assignment working in Central African Republic I was tasked by our biologist to collect in an array of 40 camera-traps. The park was very large, the size of Wales and very remote, the nearest village was a 12 hours 4×4 drive away. It was also newly proclaimed and had little in the way of infrastructure like roads. Of course, Thierry wanted to survey the areas we didn’t yet know so obviously the cameras were nowhere near any of the smatterings of roads.
He presented me with a mobile phone resplendent with a mapping app which showed the camera trap locations overlaid with our rudimentary road network. I should really say temporal track system as these so called roads consisted of two tire tracks driven through the elephant grass and mud soon to grow over again in the coming wet season. The park consists of a mosaic of wooded savannah and tropical lowland rainforest so you are either struggling through 2 meter high elephant grass or deeply tangled riverine forest growth. Added to the physical challenges of working in the park was the fact of it harboring armed Sudanese cattle herders, poachers and Lord’s Resistance Army militia.
So equipped with the mobile phone, two trackers and 5 armed rangers off we went to collect the cameras. After three hours bumpy ride plagued with biting tsetse fly we got as close to the first camera as any road was going to take us. Using the phone to navigate I pointed us in the general direction praying that the battery would last. If it failed we would be completely lost with no landmarks. Two kilometers later we had narrowed down the camera location to about 20 meters and under the vigilant eye of the rangers myself and the two trackers began searching for the camera in the thick jungle tangle.
Once the camera was reclaimed it was bagged up and we set out for the walk to the next camera another kilometer or so away. The whole day was spent battling foliage and insects in the 40o c temperatures for a total of 8 cameras. We made camp for the night; the journey back to base was just too far with so many cameras still to collect. It took 4 days to collect half the array and it was with some relief that we trundled back into base camp having had no encounter with armed men. A hot shower, something other than sardines to eat and the excitement of examining the camera-trap pictures was a just reward for all our foot work
The cameras were being used to assess what species were present in the park and as such were left up for short periods in small arrays. In the Serengeti however, there are 225 camera traps permanently running in an area of 1125 km2. Just think of the logistics involved with changing batteries, keeping vegetation trimmed back and changing SD cards. Our researchers work tirelessly to keep the project on its toes and over the next few months I will try to bring you their stories about the work we support from the comfort of our homes. We each have our part to play but together we are a team dedicated to furthering a scientific cause.
Why we do it
Congratulations, your time classifying images on Snapshot Serengeti has resulted in yet another scientific paper. Over 70,000 of you have contributed to analysing the millions of images produced by the 225 Snapshot Serengeti cameras over the last few years. Thanks to all your effort the cameras are still rolling, creating one of the longest running cameratrap studies going. This data set is so important to scientists because of the size of the area it covers as well as the length of time it has been recording for. It allows them to ask many and varied questions about a naturally functioning healthy ecosystem and in today’s changing world it has never been so important to figure out what makes this planet tick.
The paper ‘The spatial distribution of African Savannah herbivores: species associations and habitat occupancy in a landscape context’ was published last year in Philosophical Transactions B. Visit here to read the article.
The Snapshot Serengeti team argue that if we want to predict the impact of changes/ losses of large mammals in the future we need to have a quantitative understanding of a currently functioning ecosystem. It just so happens that the Snapshot data set is perfect for this. The Serengeti National Park is representative of the grass dominated Savannahs of East Africa which are home to the world’s greatest diversity of ungulate (hoofed animals) grazers.
The team present a neat graphic that shows how the various elements interact to affect herbivore habitat occupancy.
Predators, herbivores, termites, fire, grasses and trees all play a role in determining where different herbivores choose to roam.
It seems that herbivore body size is also important to habitat selection. For example large herbivores survive by bulk grazing whereas small herbivores concentrate on grazing quality over quantity. Recently burned ground results in new vegetation growth. This growth is relatively high in nutrients compared with unburned patches and the same can be found on and around termite mounds. Small herbivores were found to occupy these areas but the sparse coverage does not favour large herbivores that must eat more volume.
The paper highlights the complex relationship between predators, herbivores, vegetation and disturbance and is well worth a read. Next time you are classifying images see if you agree. Do you see many herds of zebra or wildebeest on burnt areas or is it mostly Thompson’s gazelle? It’s another way to look at the images you classify.