- Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range by James Thorsen et al., Methods in Ecology and Evolution, June 2015.
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:
- A unifying model for capture-recapture and distance sampling surveys of wildlife populations by David Borchers et al., JASA, March 2015.
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.
- Model-based approaches to unconstrained ordination by Francis Hui et al., in Methods in Ecology and Evolution, April 2015.
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 guide to Bayesian model selection for ecologists by Mevin Hooten and Tom Hobbs in Ecological Monographs, February 2015.
A very nice survey of Bayesian model selection methods from an ecological perspective.
- A spatio-temporal point process model for ambulance demand by Zhengyi Zhou et al. in JASA, 2015.
This paper was noted as an excellent example of clear exposition and an unexpected application of point process modeling.
- Bayesian function-on-function regression for multilevel functional data by Mark Meyer et al. in Biometrics, September 2015.
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.
- Estimation and accuracy after model selection by Brad Efron in JASA, September 2014.
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.