Wednesday, October 22, 2014

Walkabout Wednesday: peak meat, what math you should know, and Tiny Data.

The USA hit peak meat in 2004, Bryan McGill laid out a perfect math ecology curriculum, and Tiny Data should be all the rage in the near future.

My Health

Tom Naughton's documentary "Fat Head" was one of the first things I watched after becoming a diabetic in 2012. This week he had some thoughts on a paleo diet inspired by a bumper crop of squash.

I'm not going to start eating pasta again anytime soon, but this is an interesting small N experiment on the effects of re-heating leftover pasta on blood glucose trajectories. The key ingredient here is "Resistant Starch". I'll come back to that in some DME post soon, but I was curious what happened to RS on reheating. Cold pre-baked potato isn't that appetizing, but Bratkartoffeln are delicious.

Not sure if this is My Health or Our Environment. But regardless, USA hit "peak meat" in 2004. Too bad the prevalence of obesity and diabetes is still rising. There's that whole paradigm needing a change thing again. The dynamic visualization is from National Geographic. Very cool.

Your Education

I didn't know that student loans had been nationalized as part of Obamacare. But seems like streamlining the system and serving more students for less money is a win. Except for the banks that no longer get to make risk free loans using taxpayer dollars as capital.

"People don't buy what you do, they buy why you do it." (18 minute video) say's Simon Sinek, and so those that focus on why make great, innovative leaders. I'm going to try using this approach in the 5 year review of the School of Natural Resources at UNL. I need to think about how his "law of innovation diffusion" works in an educational environment.

In a followup to last week's post by Jeremy Fox asking what ecologists should and should not take, Bryan McGill weighed in with a great list of math topics that budding mathematical ecologists should know. I like these topics because they are the ones I see most lacking in our students here (UG and grad), and it includes topics I wish I'd studied to make my knowledge less eclectic. The comments were interesting too, and especially the link to a recent paper on a survey of ecologists about math. In the discussion Barraquand et al. argue that
For quantitative training to be successful, our results indicate that we should (1) advertise the quantitative nature of ecology earlier and (2) better connect mathematics and statistics to particular ecological problems and datasets (as suggested in Hobbs & Ogle, 2011). 
I LOVE THIS. In our NSF funded "Research for Undergraduates in Theoretical Ecology" program here at UNL we tried to get undergraduates engaged as rising freshmen. I can still remember the comment made by two alums of the intro program: when talking to a course advisor for a pre-med program they were told that mathematics wasn't necessary! The second point is why I thought an introductory UG statistics class aimed at life science students would be better than the generic course offered by the Statistics department. Of course, the statistics department didn't agree.  

Our Environment

We often hear that 97% of climate scientists think that climate change is happening and that humans are contributing to it. But where did that number come from? This op-ed in the Wall Street Journal did a bit of digging and finds reason to believe that it isn't a very robust number, if it has been properly estimated at all. The primary issue, I believe, isn't with the statement "humans are contributing" as much as whether climate scientists agree that the issue is big enough to warrant taking severe actions to limit climate change. And honestly, that's a reasonable thing to debate. In the political arena. Willingness to take risks differs among individuals, and some risks loom larger than others. For instance, I really don't worry (personally) about sea level rise. I live in Nebraska. However, if I lived on a small pacific island, sea level rise is an existential risk.

October 16 was National Feral Cat Day. In case you missed it, I wrote a blog post celebrating it, sort of.

Bryan McGill over at Dynamic Ecology wrote "We're so busy obsessing with equilibrium math models and small scale manipulative experiments we're missing a lot of the story that is sitting in front of us in the massive amounts of data that have been and could be assembled." Yep. I agree, but boy is it hard to get people to pay attention to those massive data sets. I also agree with Jeremy Fox's comeback that "If your model’s not explicit (and if you don’t care much for doing experiments), then your big data might as well be pig data." (link is Jeremy's; it's worth a read!).

This actually came out a couple of weeks ago, but IGERT fellow Trisha Spanbauer has a nice article on dynamic instability preceding a regime shift in paleo diatom communities. Very cool data that is challenging to use with the sorts of methods Trevor Hefley used to detect a transcritical bifurcation in bird populations. I need to learn more about the technique Trisha used, Fisher Information.

It would cost $51 Million to make all of conservation science published since 2000 available. I agree with the sentiment that information needs to be available for management, and quickly. On the Missouri River data on bird responses to habitat restoration was often not available until the researchers involved had published! This introduced a 3-5 year lag between when the information was collected and when it could be incorporated into models for decision making. @HugePossum thinks this is embarrassing; not sure it that's because it's so cheap and we should just do it, or because it's so expensive?

And Statistics!

I confess I sometimes get distracted by statistics. I'm also very fond of "informed" statistical models, and here's a great one to figure out how many pairs of socks one has given a sample of odd socks. I like the term "Tiny Data". In environmental management we often have to make decisions given "Tiny Data", rather than "Big Data" that everyone's raving about. 

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