R. A. Payn

Linking phylogenetic and functional diversity to nutrient spiraling in microbial mats from Lower Kane Cave (USA).

Microbial mats in sulfidic cave streams offer unique opportunities to study redox-based biogeochemical nutrient cycles. Previous work from Lower Kane Cave, Wyoming, USA, focused on the aerobic portion of microbial mats, dominated by putative chemolithoautotrophic, sulfur-oxidizing groups within the Epsilonproteobacteria and Gammaproteobacteria. To evaluate nutrient cycling and turnover within the whole mat system, a multidisciplinary strategy was used to characterize the anaerobic portion of the mats, including application of the full-cycle rRNA approach, the most probable number method, and geochemical and isotopic analyses. Seventeen major taxonomic bacterial groups and one archaeal group were retrieved from the anaerobic portions of the mats, dominated by Deltaproteobacteria and uncultured members of the Chloroflexi phylum. A nutrient spiraling model was applied to evaluate upstream to downstream changes in microbial diversity based on carbon and sulfur nutrient concentrations. Variability in dissolved sulfide concentrations was attributed to changes in the abundance of sulfide-oxidizing microbial groups and shifts in the occurrence and abundance of sulfate-reducing microbes. Gradients in carbon and sulfur isotopic composition indicated that released and recycled byproduct compounds from upstream microbial activities were incorporated by downstream communities. On the basis of the type of available chemical energy, the variability of nutrient species in a spiraling model may explain observed differences in microbial taxonomic affiliations and metabolic functions, thereby spatially linking microbial diversity to nutrient spiraling in the cave stream ecosystem.

A software tool to assess uncertainty in transient storage model parameters using Monte Carlo simulations

Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.

Early Detection of Nonnative Alleles in Fish Populations: When Sample Size Actually Matters

Reliable detection of nonnative alleles is crucial for the conservation of sensitive native fish populations at risk of introgression. Typically, nonnative alleles in a population are detected through the analysis of genetic markers in a sample of individuals. Here we show that common assumptions associated with such analyses yield substantial overestimates of the likelihood of detecting nonnative alleles. We present a revised equation to estimate the likelihood of detecting nonnative alleles in a population with a given level of admixture. The new equation incorporates the effects of the genotypic structure of the sampled population and shows that conventional methods overestimate the likelihood of detection, especially when nonnative or F-1 hybrid individuals are present. Under such circumstances—which are typical of early stages of introgression and therefore most important for conservation efforts—our results show that improved detection of nonnative alleles arises primarily from increasing the number...