Because there hasn’t been enough ocean silliness lately…
I’ve long been a fan of Ze Frank. But who knew that one of the next places he’d be turning his razor sharp with was on the natural world. And not only that, but, he’d make sure everything is pretty much scientifically kosher. So, I give you three things that nearly made me laugh so hard I was almost glad I wasn’t in a drysuit (because tight neck seals and laughing = no fun?), in escalating order of hilarity. Note, some of this is not so SFW in a mild manner, but certainly always SFDSN.
OK, folk on the Twitters – if you’re not following @DATACURATORHULK, I take no responsibility for what may happen to you. And if you’re not curating your data properly, or making sure that it is open?…hide.
I’ve been pretty stoked about the This is What a Scientist Looks Like project on tumblr. So much so that I felt compelled to submit an (old) photo of me doing field work. I mean, when one things science, they often think labcoats and microscopes. When one thinks ecology, they often think hiking in a forest or working out on a sunny grassland. (note: these are impressions I’ve gotten to people when I say these words – not what I think myself, natch.)
So, why not throw in something of what a marine ecologist at work looks like. So here’s my shot:
Yeah, I admit, it’s kind of a marine ecology beefcake shot, and definitely falls into the ¿Quien es el mas macho? school of marine ecology, but I kinda love it (and thanks to Kristin Hultgren for taking it on our wacky marine ecology roadtrip).
But I was not prepared for what was to happen next. Namely, a good friend of mine getting hold of it and showing the picture for what it really is – me making my James Bond escape after blowing up the Evil Villan’s lair.
This is totally going to be the photo on the door of my lab one day.
So what is my project going to address? Measurement error.
WOOOOOOOOO MEASUREMENT ERROR!
But wait, before you roll your eyes at me, this is REALLY IMPORTANT. Seriously!
It can change everything we know about a system!
I’m working with a 30 year data set from the Channel Islands National Park. 30 years of divers going out and counting everything in those forests to see what’s there. They’ve witnessed some amazing change – El Niños, changes in fishing pressure, changes in fishing pressure, changes in urbanization on the coast, and more. It’s perhaps the best long-term large-scale full community subtidal data set in existence (and if there are better, um, send ‘em my way because I want to work with them!)
But 30 years – that’s a lot of different divers working on this data set under a ton of different environmental conditions. Doing basic sampling on SCUBA is arduous, and given the sometimes crazy environmental conditions, there is probably some small amount of variation in the data due to processes other than biology. To demonstrate this to a general non-statistical audience, I created the following video. Enjoy watching me in my science film debut…oh dear.
OK, my little scientific audience. You might look at this and think, meh, 30 years of data, won’t any measurement error due to those kinds of conditions or differences in the crew going out to do these counts just average out? With so much data, it shouldn’t be important! Jarrett just wanted an excuse to make a silly science video!
And that’s where you may well be wrong (well, about the data part, anyway). I’ve been working with this data for a long time, and one of my foci has been to try and tease out the signals of community processes, like the relative importance of predation and grazing versus nutrients and habitat provision. Your basic top-down bottom-up kind of thing. While early models showed, yep, they’re both important, and here’s how and why, some rather strident reviewer comments came back and forced me to rethink the models, adding in a great deal more complexity even to the simplest one.
And this is where measurement error became important. Measurement error can obscure the signal of important processes in complex models. A process may be there, may be important in your data, but if you’re not properly controlling for measurement error it can hide real biological patterns.
For example, below is a slice of one model done with two different analyses. I’m looking at whether there are any relationships between predators, grazers, and kelp. On the left hand side, we have the results from the fit model without using calibration data to quantify measurement error. While it appears that there is a negative relationship between grazers and kelp, there is no detectable relationship between predators and grazers (hence the dashed line – it ain’t different from 0).
This is because there is so much extra variation in records of grazer abundances due to measurement error that we cannot see the predator -> grazer relationship.
Now let’s consider the model on the right. Here, I’ve assumed that 10% of the variation in the data is due to measurement error (i.e., an R2 of 0.9 between observed and actual grazer abundances). So, I have “calibration” data. This error rate is made up, just to show the consequences of folding the error in to our analysis.
Just folding in this very small amount of measurement error, we get a change in the results of the model. We can now see a negative relationship between predators and grazers.
I need this calibration data to ensure that the results I’m seeing in my analyses of this incredible 30 year kelp forest data set are real, and not due to spurious measurement error. So I’m hoping wonderful folk like you (or people you know – seriously, forward http://scifund.rockethub.com around to everyone you know! RIGHT NOW!) will see the video, read the project description, and pitch in to help support kelp forest research.
If we’re going to use a 30 year data set to understand kelp forests and environmental change, we want to do it right, so, let’s figure out how much variation in the data is real, and how much is mere measurement error. It’s not hard, and the benefits to marine research are huge.
Whew, it’s been a bit since I posted here. Rest assured, little sea squirts, there are some interesting new things in the works. Some things that are science-y (in which I try and use this blog as a sounding board/ lab notebook) and some things not so much.
In the not so much category, a ton of my time has lately been going to the organizing of the #SciFund challenge – a large initiative to try and crowdfunding for science! If you haven’t been following it, check out our initial manifesto here.
I’m pretty stoked about the whole thing – it’s a real way of connecting science to the public via a funding mechanism. And with us launching on November 1st, it’s been an absolute pleasure to watch the creative and innovative videos that participating scientists have been putting together to solicit funds.
Videos, you say? Am I doing one?
Why yes! So to give you a hint of what’s to come, here’s a brief preview of my #SciFund video. I think you’ll all agree, it’s vintage me, attempting to sell one of the more arcane (to the public) pieces of my research in a way that might just connect. We shall see.