Paper of the year 2018

The paper-of-the-year competition sees Eco-Stats members nominate their favourite article hoping to win "free coffee for a year". This year saw a relaxation of some of the previous requirements - no longer did the paper need to be about ecology or statistics; or even be published; in fact one entry was even from the end of 2017. The result was a wide field - from LEGO investors; to fire wielding sh*t hawks; to the earth getting into a bit of a (blueberry) jam. After much debate the results are in:

The winning paper was:


Anders Samberg (2018) Blueberry Planet.

arXiv:1807.10553 [physics.pop-ph]

This paper was nominated by Gordana Popovic because:

It shows very unpretentiously what research involves. You have a question, you find all the research in the area, you mush the two together, you get an answer.


Honourable mentions go to:


Bonta, M et al. (2017) Intentional Fire-Spreading by “Firehawk” Raptors in Northern Australia. Journal of Ethnobiology

This paper was nominated by Ben Maslen because:

I like this paper as it outlines a very intriguing and at first glance outlandish ecological behaviour that combines empirical evidence with indigenous ecological knowledge. Birds spreading fires, who knew!


Dobrynskaya, Victoria and Kishilova, Julia (2018) LEGO - The Toy of Smart Investors.

This paper was nominated by Michelle (Shi Jie) Lim because:

I like this paper because Lego toys do not belong to the luxury segment and are affordable to most retail investors. Although the returns may not be significant in reality, the study shows that people are willing to pay a premium for Lego sets. Any Lego toy owner would probably find this paper relatable.


The other nominations in no particular order are:


Zhu and Bradic (2018) Linear Hypothesis Testing in Dense High-Dimensional Linear Models. Journal of the American Statistical Association.

This paper was nominated by David Warton because:

...developing an original new machinery for inference and applying it to a tricky problem, that of simultaneous inference for lots of parameters.


Miller and Sanjurjo (2018) Surprised by the Hot Hand Fallacy?A Truth in the Law of Small Numbers. Econometrica.

This paper was nominated by Robert Nguyen because:

I think this is interesting because it tackles something that generally people believe (there is a hot hand in sport) but as of yet there is no evidence it exists or is there?


Fraser et al. (2018) Questionable research practicesin ecology and evolution. PLoS ONE.

This paper was nominated by Mitchell Lyons because:

I like these types of papers, and there’s been a few lately, including some recent press on some editorials in Nature (Google those if you like. It helps me to gain context on why people (we, and me now) in ecology and evolution think about significance the way they do.


Yixin Wang & David M. Blei (2018): Frequentist Consistency of Variational
Bayes. Journal of the American Statistical Association.

This paper was nominated by Elliot Dovers because:

I like this paper because it provides a firmer theoretical footing for a technique (more generally than has been previously for variational approximations) that has perhaps been used willy-nilly for a long time in computer science (I'm not innocent here either). I also like that the authors give time to addressing (and linking) the technique with respect to both a frequentist and Bayesian approaches. The result "bridges the gap in asymptotic theory between the frequentist variational approximation, in particular the variational frequentist estimate (VFE), and variational Bayes"... good to see authors with a less binary take in on what it means to be a statistician.

Template Model Builder Tutorial

Many of the Eco-Stats group are using Template Model Builder (TMB) - a very flexible package in R for fitting all sorts of latent variable models quickly. For R users without any C++ coding experience, getting familiar with the package might be a little daunting so we've put together a gentle introduction with some simple examples. Follow the link below and get going with TMB:

TMB Introduction Tutorial

Note: before installing TMB (by your usual means of installing an R package) compiling C++ code will require a working development environment. In Windows you can just install the latest version of Rtools - follow the install guide here. If installing on Mac OS or Linux - following the devtools install guide will do the trick - check it out here.

Paper of the year 2017

The competition for paper of the year 2017 was heated, with the ecostatistician proposing the winning paper scoring the coveted "free coffee for a year" prize, The nominations were diverse, all the way from pure ecology to very fancy stats. After much debate, the winner was:

Hallmann CA, Sorg M, Jongejans E, Siepel H, Hofland N, et al. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLOS ONE 12(10)

This paper was nominated by John Wilshire, who summarises it as follows:

Flying insects play a very important role in ecosystems, both as pollinators and as food sources for other animals. This paper shows that their populations have massively declined over a relatively short period of time  (at least in protected areas in Germany). I like this paper as it presents the results of a long term study, and it is a pretty scary example of the impacts we are having on ecosystems. Plus it is open access and has data and code available, and the statistical analysis is presented in a clear and easy to follow manner.

Other nominees were (in no particular order):

Special Feature in Methods in Ecology and Evolution on Eco-Stats '15

There is a Special Feature in the April 2017 issue of Methods in Ecology and Evolution reporting outcomes from the Eco-Stats '15 conference, blog post about it here

https://methodsblog.wordpress.com/2017/04/12/generating-new-ideas/

Three simple things you should know about interpreting linear models

What do the coefficients from linear models actually mean? What does it mean to "control for" another variable. How do I interpret coefficients in the presence of other variables v.s. in a model on their own? What do coefficients mean in a generalized linear model? How do I standerdise data using models? What are offsets, and how do I use them? 

Learn the answers to these and many more questions this Friday, October 21 at 2pm. 

Materials for this lab can be found here.

Introduction to mixed models with lme4

Ever wondered what a mixed model is? Have a nested design for your experiment and don't know how to analyze it? Confused by fixed and random effects?   What's the deal with lme4?

Come and find out the answer to these questions (and more) in an hour. 

Friday 2-3 PM in Biology 640.

Materials are available here.

Congrats to Gordana Popovic for winning a Student Prize for her awesome talk at ISEC in Seattle. She spoke about an algorithm for covariance modelling of discrete data using copulas (which are fun). For her efforts she got a certificate, an Amazon voucher and one of these...