Grant Proposal Writing
We’ve recently been working on a grant proposal to continue our camera trap project past 2012. Grant proposal time is always a little bit hectic, and particularly so this time for Ali, who, while running around Arusha to get research permits and supplies and get equipment fixed, has also been ducking into Internet cafes to help with the proposal. This proposal is going to the National Science Foundation, which has funded the bulk of the long-term Lion Project, as well as the first three years of the camera trap survey.
The proposal system is two-tiered. First we submit what is called a “pre-proposal” – a relatively short account of what we want to study and why, along with researchers’ credentials. This is the proposal that’s due today. Over the next six months, NSF will convene a panel to review all the pre-proposals that it receives and will select a fraction of them to invite for a “full proposal” due in August. If we get selected, we will then have to write up a more extensive proposal, describing not only what and why we want to do this research, but also exactly how we’re going to do it and how much money we require. Then another panel is convened to review these proposals, with the results reported in November or December.
Proposals are always helped by “preliminary data” – that is, data that’s not yet ready for publication, but gives a hint at a research study’s power. So we’ve taken the Snapshot Serengeti classifications for Seasons 1-4, run a quick-and-dirty algorithm to pull out images of wildebeest and hartebeest, and then stuck the results on maps, grouped by month. The size of the circles shows how many wildebeest or hartebeest were seen that month by a camera. The background colors show ground vegetation derived from satellite images, so green means, well, the vegetation is green, whereas yellow means less green vegetation, and tan means very little green vegetation.
Wildebeest in the dry season (Season 1 and Season 3 on Snapshot Serengeti)
Hartebeest in the dry season (Season 1 and Season 3 on Snapshot Serengeti)
Wildebeest in the wet season (Season 2 and Season 4 on Snapshot Serengeti)
Hartebeest in the wet season (Season 2 and Season 4 on Snapshot Serengeti)
(You can click on any of these images to see a larger version.)
These maps show variation from month to month and season to season in the greenness of the vegetation and the response of the grazers to that vegetation. They also show that these patterns vary from year to year. We’ve used this variation as a foundation to our proposal: how do these different patterns in vegetation that vary over time affect the grazers in the Serengeti? How do the variations in grazers affect the predators?
What questions spring to your mind when you look at these maps?
21 responses to “Grant Proposal Writing”
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Snapshot Serengeti 
Why is Nov 2011 so different from Nov 2010 for Wildebeest?
We believe it’s because of a difference in rainfall. You can see that in 2011 there’s more green and yellow on the map, which means more grass. This fresh grass has a lot of nutrients in it and which the wildebeest are craving after a long dry season. So they’re coming out to eat this fresh grass. In 2010 the rains came later, so in November there wasn’t much grass for them to eat yet.
I wonder what the data would look like as a time-lapse film? If you could output a map for each day (instead of month) then you should get a movie showing the ebb and flow of vegitation and fauna. A program like ‘ffmpeg’ can do this. You could use differnt colour dots for predators? MIght help get that funding flowing?
A time-lapse film would be very interesting indeed. I think we’d probably need to do it weekly, rather than daily because of the availability of satellite data and we get better camera trap data when it’s pooled over time. But weekly would still give us a lot of frames. Thanks for the program recommendation. And yes, I think the film would be impressive to funders, but we’d still need to do all the writing, too!
I’m a computer programmer. I’m probably not alone? So naturally I was thinking about how volunteeer programmers would write an animation. Seems to me that if we knew the data format, and had a sample of data we could write such digital ‘experiments’. Researchers would then get the programs to play with on the real data. But I think the sample data should be faked.
Not everyone can be trusted with real data. I think researchers should be very careful giving access to the project data. Wildlife needs to be protected. Information about the movement of endangered species should be kept protected also.
GRANT PROPOSAL WRITING
Hi, it may be a no-no, but since you have so many ( obviously keen ) participants in the project, why not tap into the group as a crowd-funding source?
We might do that at some point. Keeping the lion project and the camera project going full-time are quite expensive, though, and probably out of the range of what we could raise through crowd-funding.
This is fascinating!
These are probably naive questions / ponderings 🙂 but a couple of things jump out at me from these maps:
1. The huge difference in wildebeest numbers between dry seasons 2010 and 2011. I can see there is more green vegetation in Nov 2011, but is that the only reason for the remarkable difference in wildebeest numbers between Nov 2010 and Nov 2011?
2. During the wet seasons, it looks like both wildebeest and hartebeest numbers go down when the extent of green vegetation increases beyond a certain point (ref the May maps). Again, I’m intrigued to know why that is, and what factors might be involved there.
1. Yes, we think so. The wildebeest are particularly keen to get out onto the plains grass because that grass is more nutritious than the woodland grass. (I’ll do a post about this sometime; it’s part of what fuels the wildebeest migration.)
2. Yes, I find that fascinating, too. And I’m really not sure what the reason for it is. It might have to do with grass height. The grazing species each have their own preference for grass height which has to do with the way their mouths are shaped. So, for example, Thomson’s gazelles only eat really short grass and it’s thought that they “follow” the wildebeest and zebra to nibble down the already short grass even shorter. So it’s possible that if the grass gets too high (where it’s very green) that the wildebeest will ignore it for more medium-height grass. But this is just a wild guess; I’ve never seen the grass really super high on the plains, so it doesn’t seem totally likely. This question, though, is something that we’ll be investigating further.
Ahh… yes I can see how grass height could be a factor there, both directly in terms of grass “eatability” (is that a real word?! Heh!), and perhaps indirectly in terms of how it might influence other animals’ behaviour.
I would be interested to know how water sources affect the seasonal distribution of grazers (and predators). Some cameras were clearly near water sources (wet elies and giraffe with muddy legs up to their knees) although they were not pointed at the water. Is it possible to add key water sources to the maps?
Also, is it possible to distinguish between grass plains and bush in the green areas on the maps? This habitat difference would also affect distribution of certain browsers and grazers.
Thank you for a great project and for including us.
Those are all really good points and questions. The short answer is yes. We have the locations of waterways, both year-round ones (which are few in our camera trap area) and ones that dry up in the dry season. Our research questions about where the grazers are located involve using what we call “landscape features” and includes water sources, kopjes (the rocky outcrops you see in some images), as well as things like general topography and soil moisture. We also have some information about plains vs. bush/trees for our camera trap area. Ali has classified the area immediately surrounding each camera and there is a large-scale map of habitat types for Serengeti. But we also want to explore the intermediate range — what’s in the few square kilometers around each camera — and we’re hoping to get funding to do that.
It’s been a privilege to join you on the virtual safari around the park, but one thing I can’t understand is why, after all the evolution the grass eaters are not present in more or less equal number? Is migration the key that has allowed the wilderbeast to proliferate while the topi and similar are relatively rare?
That’s a really interesting question. In this particular system it may well be that the migration allows the wildebeest to rise to great numbers (though it’s probably more complicated than just migration). But in every species community out there, there’s an imbalance in the populations of species. For example, I also do work in the U.S. Midwest tallgrass prairies. There are over a dozen grass species in an intact prairie, but the community is dominated by big bluestem, little bluestem, and indiangrass. If you think about your favorite forest system, too, you’ll realize it’s dominated by a few tree species even though there are likely many other tree species that aren’t as numerous.
But: why? This is a big question in ecology and evolution, and if you do a google search for “species abundance distribution” you’ll see tons of scholarly papers at the top of your search. If you’re interested in learning more about how this imbalance in species abundance is studied, you can read about it in Nature Education.
Any chance you could do a blog post about the relative incidence of categorizations of each animal type? I.e. first remove the ca. 70% of capture events which were “nothing here” and then give the relative percentage of the remaining capture events which were “wildebeest” or “wildebeest and something else”, “zebras” or “zebras and something else”, etc. Fairly high on the list would probably be “not possible to determine” and very low on the list would be rhinos and zorillas. Was there even one reliable capture of a zorilla in the whole of season 4? I think such a list could be quite interesting!
Yes, I can do that. I am still working out the “data reduction” algorithm for the hardest to identify images. But I could do some rough estimates for in the meantime. I think “not possible to determine” might be a lot lower than you’d expect, and I agree that rhinos and zorillas will be at the bottom.
Super! Even rough estimates would be great to see. Something to look forward to (as we wait for season 5) and thanks!