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Hansen, R. R.

Publications and source records attributed to Hansen, R. R..

2 recordsLinked to original sources

Model-based ordination of pin-point cover data: effect of management on dry heathland

Recently, there has been an increasing interest in model-based approaches for the statistical modelling of the joint distribution of multi-species abundances. The Dirichlet-multinomial distribution has been proposed as a suitable candidate distribution for the joint species distribution of pin-point plant cover data and is here applied in a model-based ordination framework. Unlike most model-based ordination methods, both fixed and random effects are in our proposed model structured as p-dimensional vectors and added to the latent variables before the inner product with the species-specific coefficients. This changes the interpretation of the parameters, so that the fixed and random effects now measure the relative displacement of the vegetation by the fixed and random factors in the p-dimensional latent variable space. This parameterization allows statistical inference of the effect of fixed and random factors in vector space, and makes it easier for practitioners to perform inferences on species composition in a multivariate setting. The method was applied on plant pin-point cover data from dry heathlands that had received different management treatments (burned, grazed, harvested, unmanaged), and it was found that treatment have a significant effect on heathland vegetation both when considering plant functional groups or when the taxonomic resolution was at the species level.

ecology

Simultaneous Discovery of Positive and Negative Interactions Among Root Microbiome Bacteria Using Microwell Recovery Arrays

Understanding the consequences of microbe-microbe interactions is critical in efforts to predict the function of microbiomes and to manipulate or construct communities to achieve desired outcomes. The investigation of these interactions poses a significant challenge - in part due to the lack of suitable experimental tools. We present the Microwell Recovery Array, a high throughput approach designed to rapidly screen interactions across a microbiome and uncover higher-order combinations of strains that either inhibit or promote the function of a GFP-producing focal species. One experiment generates 104 unique microbial communities that contain a focal species combined with a unique combination of previously uncharacterized cells from plant rhizosphere. Cells are then sequentially extracted from individual co-culture wells that display highest or lowest levels of focal species function using a novel high-resolution photopolymer extraction system. Microbes present are subsequently identified and the putative interactions are validated. Using this approach, we screen the Populus trichocarpa rhizosphere for bacterial strains affecting the survival and growth of Pantoea sp. YR343, a plant growth promoting strain isolated from the P. trichocarpa rhizosphere. We were able to simultaneously isolate and validate multiple Stenotrophomonas strains that antagonize strain YR343 growth and a set of Enterobacter strains that promote strain YR343 growth. The latter demonstrates the unique ability of the platform to uncover multi-membered consortia that generate emergent phenotypes. This knowledge will inform the development of beneficial consortia that promote the production of Populus biofuel feedstock, while the platform is adaptable to screening higher-order interactions in any microbiome of interest. Significance StatementAchieving a fundamental understanding of microbe-microbe interactions that occur within microbial communities is a grand challenge in microbiology due to the limited experimental tools available. In this report, we describe a new tool that enables one to screen microbial interactions across thousands of compositionally unique communities to discover collections of bacteria that antagonize or promote the survival and growth of bacteria with important functions. This approach has the unique ability to uncover higher-order combinations of bacteria that generate emergent phenotypes, information useful for development of biofertilizer, biocontrol, or probiotic consortia, as well as in the design of communities for biosynthetic compound production.

bioengineering