Efficient Markov chain Monte Carlo sampling for hierarchical hidden Markov models - Daniel Turek; Perry de Valpine; Christopher J. Paciorek (Environmental and Ecological Statistics, 2016)

Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass - Erin E. Nuccio; James Anderson‐Furgeson; Katerina Y. Estera; Jennifer Pett‐Ridge; Perry de Valpine; Eoin L. Brodie; Mary K. Firestone (Ecology, 2016)
The interface between roots and soil, known as the rhizosphere, is a dynamic habitat in the soil ecosystem. Unraveling the factors that control rhizosphere community assembly is a key starting point for understanding the diversity of plant‐microbial interactions that occur in soil. The goals of this study were to determine how environmental factors shape rhizosphere microbial communities, such as local soil characteristics and the regional climate, and to determine the relative influence of the rhizosphere on microbial community assembly compared to the pressures imposed by the local and regional environment. We identified the bacteria present in the soil immediately adjacent to the roots of wild oat ( Avena spp .) in three California grasslands using deep Illumina 16S sequencing. Rhizosphere communities were more similar to each other than to the surrounding soil communities from which they were derived, despite the fact that the grasslands studied were separated by hundreds of kilometers. The rhizosphere was the dominant factor structuring bacterial community composition (38% variance explained), and was comparable in magnitude to the combined local and regional effects (22% and 21%, respectively). Rhizosphere communities were most influenced by factors related to the regional climate (soil moisture and temperature), while background soil communities were more influenced by soil characteristics ( pH , CEC , exchangeable cations, clay content). The Avena core microbiome was strongly phylogenetically clustered according to the metrics NRI and NTI , which indicates that selective processes likely shaped these communities. Furthermore, 17% of these taxa were not detectable in the background soil, even with a robust sequencing depth of approximately 70,000 sequences per sample. These results support the hypothesis that roots select less abundant or possibly rare populations in the soil microbial community, which appear to be lineages of bacteria that have made a physiological tradeoff for rhizosphere competence at the expense of their competitiveness in non‐rhizosphere soil.

Monte Carlo estimation of stage structured development from cohort data - Jonas Knape; Perry de Valpine (Ecology, 2016)
Cohort data are frequently collected to study stage‐structured development and mortalities of many organisms, particularly arthropods. Such data can provide information on mean stage durations, among‐individual variation in stage durations, and on mortality rates. Current statistical methods for cohort data lack flexibility in the specification of stage duration distributions and mortality rates. In this paper, we present a new method for fitting models of stage‐duration distributions and mortality to cohort data. The method is based on a Monte Carlo within MCMC algorithm and provides Bayesian estimates of parameters of stage‐structured cohort models. The algorithm is computationally demanding but allows for flexible specifications of stage‐duration distributions and mortality rates. We illustrate the algorithm with an application to data from a previously published experiment on the development of brine shrimp from Mono Lake, California, through nine successive stages. In the experiment, three different food supply and temperature combination treatments were studied. We compare the mean duration of the stages among the treatments while simultaneously estimating mortality rates and among‐individual variance of stage durations. The method promises to enable more detailed studies of development of both natural and experimental cohorts. An R package implementing the method and which allows flexible specification of stage duration distributions is provided.