Thursday, 18 February 2016

Paper of the year 2015

So we got together recently to discuss the papers we most enjoyed in 2015, and to vote one of them the Ecostats Paper of the Year. Top prize (nominator exempt from buying the next round of coffees or beers) went to James Thorsen's spatial factor analysis paper:

Joint species distribution models fulfil a need for ecological models that describe how species distributions are simultaneously affected by habitat and communities. Thorsen et al. combined spatial models with latent factors, and applied it to the US Breeding Bird Survey and to a dataset consisting of trawler transects for rockfish along the west coast of the US.

Other nominees were:

Capture-recapture and distance sampling are both common approaches to estimating the size of a population under study. Borchers et al. have developed a statistical framework which unifies the two, by introducing a spectrum for the spatial precision of individual observations.

Unconstrained ordination methods are widely used in exploratory analysis to identify similar and dissimilar habitats, communities, or study sites. By developing a model-based framework for ordination, Hui allows ordination to be used for statistical inference rather than purely descriptive uses.

 A very nice survey of Bayesian model selection methods from an ecological perspective.

This paper was noted as an excellent example of clear exposition and an unexpected application of point process modeling.

Nominated as a change of pace that is likely outside the comfort zone of statistician used to analyzing typical ecological data, Meyer et al. describe a procedure for analysis of data that consist entirely of functional objects.

Efron's paper snuck in even though it was published in 2014. Model selection procedures like the LASSO are extremely popular among statisticians and ecologists alike, but the classical likelihood-based standard errors often don't apply post-model-selection. This paper proposes a general resampling-based theory of standard error estimation after model selection.

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