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):

JF Drazan, AK Loya, BD Horne, R Eglash (2017) From sports to science: Using basketball analytics to broaden the appeal of math and science among youth: 2017 MIT Sloan Sports Analytics Conference

Nominated by Robert Nguyen:

I like it because its a great way to engage people that might not consider a STEM career as a possibility. I like how it gets people involved in the data collection as a starting point for engagement into the subject and goes through to a tangible final outcome for kids. But mainly I love its pro social benefit of combining something kids love (sports) with something they can consider as a future career (STEM).
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Chi, E. C., Allen, G. I. and Baraniuk, R. G. (2017), Convex biclustering. Biom, 73: 10–19. doi:10.1111/biom.12540

Nominated by David Warton:

This paper proposed penalised least squares, using a relatively simple fusion penalty, for biclustering of data, to simultaneously cluster observations and variables.  They showed desirable statistical and computational properties, it has a cool name (COBRA), and is a simple yet powerful idea.  It would be interesting to try and extend this method to handle multivariate abundance data.

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Illian, J.B. & Burslem, D.F.R.P. AStA Adv Stat Anal (2017) 101: 495. 

Nominated by Elliot Dovers:

I like it because it gives a nice and fairly concise summary of the current use of spatial point process models within ecological research. While it occasionally sounds a little like bemoaning the lack of use of these models, Illian and Burslem offer a measured thoughts on how statisticians (perhaps even outside these particular models) can engage with practitioners of the applied sciences to enable the use of statistical methodologies that have previously been labelled too difficult or complex.

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Falster, D. S., Brännström, Å., Westoby, M., & Dieckmann, U. (2017). Multitrait successional forest dynamics enable diverse competitive coexistence. Proceedings of the National Academy of Sciences:

Nominated by Mitchell Lyons:

I love the way this paper captures the thought process that neutral theory seems like a hand wave that is rather unflattering, but that current niche differentiation theory doesn’t work. So by developing a more complex combination of successional niche models that include more realistic mechanisms (size-structured growth and physiological trade-offs i.e. traits) that can differentiate along 2 axes (most consider only 1), a model based on niche theory can actually predict (~for the first time) the levels of diversity we observe. If you scroll right down, there’s some pretty neat (and accessible, given I can understand it) math behind the expectation of the height distribution of plants over time, and then all the little biological things you need to get there. Impressive in novelty and in shear work putting it all together. There’s also a n R package you can have a play with.

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Patterson, T.A., Parton, A., Langrock, R. et al. AStA Adv Stat Anal (2017) 101: 399. 

Nominated by Gordana Popovic:

This paper does a nice job demystifying animal movement models, by describing the most commonly used models, and when they can, and more importantly cannot, be used. It draws some connections between state space models and hidden Markov models, which helped me understand when they can be applied. I also love that the paper manages to be simple enough to read for an ecological audience, but give enough detail for a statistical audience.