Well, I woke up this morning, fired up Floating Forests, as is my wont, and saw this! I thought it would be a few more days, and was even going to post some exhortation, but you guys have been too awesome and brought us to 2 million classifications yourself!
Nice work, all! And now it looks like we’re going to need to throw some new regions your way soon!
A lot of what we’ll be working on to determine area of beds are heatmaps of users selecting a pixel as kelp. This sounds somewhat abstract, so I wanted to operationalize it for you with some images. Let’s start with a single image from Floating Forests chosen because it has been flagged as having kelp. It has 13 classifications, so, one more and it is ‘complete’ – unless we decide to lower the classification threshold. The image is
So, what would it look like if we overlaid all of the outlines of users outlining kelp from the other day on the image?
You can see, to some extent, folk circling the same areas, and their varying degrees of specificity. What does this result in if we want a heatmap of number of users selecting each pixel on which to do our analysis? Well, here you go!
Next time, a more quantitative look.
For the next post or three, I’m going to talk about what I see when I look at the data from one image. In the coming weeks, I hope to get at putting together bigger spatial or temporal results. But for the moment, I’m going to begin with what we see when we look at user classifications of one image. I’m going to begin with something beautiful – human variation.
This is the variability from person to person that we see in circling the same set of beds. I just find it striking and lovely.
Well, we’ve finally hit a critical mass of classifications (well, blown past it) and other projects by science team members have boiled down (we’ll be posting about them – they’re kelpy!), so we’ve begun to dig into the data. For anyone who wants to follow along at him, all code that we talk about will be posted in this github repository.
I thought I’d begin by telling you all about how *you* have been interacting with Floating Forests. Namely, how much effort do the ~5,100 users of FF put into FF the project
Many Zooniverse projects do well from a lot of people doing just a few images each. We’re no different. We have a nice distribution of folk with many doing few images (~1,500 have done just one classification), but with a looong tail with many users in the 100 to 1000 range. See below, but note the log10 scale on the x-axis.
The average user, though, does ~125 classifications. If we put it together and look at the cumulative percentage of classifications done by users who classify different numbers of images, we see that ~25% is done by those users who classify less than ~250 images. So, our ‘super-users’ are incredibly important! Heck, we have one users who has contributed 5.15% of the classifications. The top 10 have contributed 18% of classifications.
It may still be difficult to see just how much those users are doing in comparison to users classifying only a few images. So, we’ve done what many other zooniverse projects have done, made a treemap!
It’s not only incredibly informative – with the size of each square being proportional to the contribution of an individual users – but, oh, pretty data! Enjoy!
One of the things we love about Floating Forests is how simple it is, making it a great tool for classrooms. Just circle some kelp! And after only a few images, one begins to get a sense of some basic kelp biology – it’s close to the coast in shallow waters, we see less of it in the middle of winter, in some places we see less of it in later years than earlier.
This simplicity beguiles a wealth of concepts both simple and complex. One can use Floating Forests as a tool to teach basic environmental biology, population dynamics, or the ecology of climate change. Or one can use Floating Forests as a jumping off point for a classroom of kids interested in the ocean.
We’ve been lucky enough to start to interact with some great educators. We’re hoping to begin posting lesson plans for levels from elementary schools to college over at Zooteach. Here’s one of the first pieces to emerge from Fran Wilson’s wonderful 2nd grade class!
Here over at Floating Forests, we’re constantly talking about how much we love kelp. And now, in this month’s issue of Polar Research we find another example of organisms who love Giant Kelp – definitely more than us.
In this this great piece by Rosenfeld et al., show that the Patagonian squid Doryteuthis (Amerigo) gahi use giant kelp in Chile and Argentina as a place to lay their eggs. It’s the first evidence of this happening in the Magellanic channels of the sub-Antarctic. This happens in the Falkland Islands, too on both Giant Kelp and the subcanopy kelp Lessonia. But not in places like, say, California.
It’s a cool story, and Resenfeld et al. provide some great pictures! Check it out. And know that be love of kelp knows no species bounds!
Rosenfeld, S., J. Ojeda, M. Hüne, and A. Mansilla. 2014. Egg masses of the Patagonian squid (Doryteuthis Amerigo gahi) attached to giant kelp (Macrocystis pyrifera) in the sub-Antarctic ecoregion. Polar Research. 10.3402/polar.v33.21636
One of the fun things about the FF project is that the science team is distributed all over the world – from Portugal to Boston to Santa Barbara to Chile. Recently, I got to go and work with Alejandro on some analyses of kelp removals on fish, and he took me out for a dive at his field site. It’s not giant kelp (that’s further south in Chile – and you’ll be seeing those images soon!) but Lessonia forms these awesome underwater forests that look like a bonsai Macrocystis forest. Ale shot some video of out explorations around an area that had been subjected to some strong snail herbivory, so I thought I’d share it with y’all.
Welcome back to Floating Forests! We’ve had a few snafus that have been lowering the image quality that folk have mentioned, and we’ve got them locked down! What issues, some of you might be saying? During an image upload, something got snagged in the color translation, so images were coming out looking dark and red. Some users (thanks, artman40!) were quite skilled at seeing the kelp anyway. Hats off to you!
We’re actually kind of excited about this, as it will give us a second validation dataset so we can really calibrate user views of images under different conditions. Some of the images that we have up there have been previously been viewed by research assistants (undergrads) at UC Santa Barbara. Part of our analysis of the data involves calibrating against any noise of different viewers looking at the same image. Now we’ll have three different classifications types for a set of the images – undergrad RAs, Floating Forest users, and Floating Forest users with a color-skewed dataset. It’s going to help us reduce error of estimation of kelp bed size and get better quality data in the end as we build better models of the data. There’s actually a nice article on just this topic in last month’s issue of Methods in Ecology and Evolution (one of my favorite journals!) (what, don’t look at me that way, I’m a scientist!)
A number of folk have asked us about the color skewing, and also asked us about the algorithm we use to select images. Stay tuned, as we’re working with Zooniverse to release the code they use to select images, and then anyone who is interested in having at it – either for their own applications (say, spotting coral reefs in the tropics, and needing to subset out only coastal images) or who is interested in trying to make our process better (and reduce the number of land images while not losing coastline images). We’d love to collaborate with more folk out there!