Eco-Stats Paper of the Year, 2013

We just had our second annual paper of the year competition, highlighting papers that made an impression to UNSW Eco-Stats researchers over the previous year.  Papers were supposed to be in print in 2013, but this was interpreted generously. And the nominees are...

• Sengupta & Cressie (2013), "Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions", Spatial Statistics which is a nice example of how you can do hierarchical modelling in a frequent framework via the EM algorithm. A lot of maths in it though... 
• Williams et al (2013), "The ice age ecologist: testing methods for reserve prioritization during the last global warming", Global Ecology & Biogeography, evaluating methods of reserve design by looking at the success of conservation efforts by an ecologist based in the last ice age (!?) in managed the climate change risks they faced, as informed by current climate and species distributions. 
• Efron (2012), "Bayesian inference and the parametric bootstrap", Annals of Applied Statistics, Making connections between frequentist and Bayesian techniques, in this case using the parametric bootstrap to construct posterior distributions, which offers insights into choice of prior. 
• Zhou et al (2013), "Tensor Regression with Applications in Neuroimaging Data Analysis", Journal of the American Statistical Association, something a little different, developing a framework for regression using neuro-imaging data as predictor variables. These datasets are huge but highly structured across dimensions, which is capitalised upon here using tricky tensor product calculations. 
• Diez et al (2013), "Probabilistic and spatially variable niches inferred from demography", Journal of Ecology, an interesting paper merging demography and SDM ideas, predicting population growth rate spatially by modelling demographic parameters as a function of environment, and comparing to observed distribution. 
• Giorgi et al (2013), "Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models", preprint on ArXiv, taking information from different spatial datasets where there is sampling bias in some but not others, such that one can account for it through joint modelling. Relevant to SDMs, where you have a mixture of systematically and opportunistically collected data. 

but the winner is... Illian et al (2012), "Fitting complex ecological point process models with integrated nested Laplace approximation", Methods in Ecology and Evolution, showing how a funky approximation technique (INLA) which is all the rage can be used to compute Bayesian posteriors and hence make inferences about fancy point process models that would be difficult to fit otherwise. Keeps most of the technical stuff to appendices, so some meaty stuff is there but the paper remains accessible to a broad audience.