Constructing the Heroic Whistleblower: A Social Scientific Approach

Many whistleblowers perform heroic acts, but not all whistleblowers are heroes. Motivation, method, and risk vary across whistleblower contexts. Although many whistleblowers portray aspects of archetypal heroism, research is needed to specify the qualities of heroic whistleblowers from non-heroic whistleblowers. The present study aims to develop an archetype of heroic whistleblowers. We identify five dimensions of whistleblowing heroism and then draw upon data from interviews that we conducted with 32 actual whistleblowers to provide examples of each element. We argue there are five dimensions of the whistleblowing process that distinguish heroic whistleblowers. The five dimensions include 1) motivation for blowing the whistle (altruistic vs. selfish), 2) complicity in the wrongdoing (bystander vs. complicit), 3) level of risk for exposing the wrongdoing (high risk vs. low risk), 4) whistleblower effect (efforts led to positive change vs. efforts produced little or no change), and 5) whistleblower willingness (they would blow the whistle again vs. they would not blow the whistle again). We argue whistleblowers exemplify heroism when they expose wrongdoing for altruistic reasons, are not complicit in the unethical behavior, they assume a high level of risk to their safety, reputation, or career, when their efforts lead to constructive changes, and when the whistleblower remains vigilant in their willingness to combat wrongdoing. We conclude by offering propositions, limitations, and future research possibilities

Nested Scales of Spatial and Temporal Variability of Soil Water Content Across a Semiarid Montane Catchment

Topographic redistribution of water has been represented by various terrain metrics (e.g., topographic wetness index, slope, and upslope accumulated area). This type of landscape characterization has promoted the use of terrain metrics to inform how spatial patterns of soil volumetric water content (VWC) influence streamflow, ecological processes, and associated nutrient fluxes. However, evaluation of what these static terrain metrics reflect has only been accomplished in a few catchments. Additionally, previous research suggests that relationships between topographic metrics and VWC could be different across catchments through time. Here we measured VWC from snowmelt through summer drydown across a semiarid montane catchment. Using a spatially nested sampling design, we assessed the spatiotemporal variability of VWC from plot (tens of meters) to landscape scales (hundreds of meters). Variance of riparian area VWC increased as the catchment dried, while upland variance decreased, highlighting the utility of delineating distinct landscape units when considering spatial variability of moisture, rather than calculating statistics across the landscape as a whole. In contrast to previous research, we found that the relationship between VWC and topographic metrics persisted through the dry catchment state, suggesting that patterns of topographic redistribution of water during snowmelt continued to influence dry season VWC despite variability in plot scale vertical processes (e.g., evapotranspiration). Future research should focus on resolving the relationship between catchment moisture state and VWC variability as a function of wetness state, seasonality, and magnitude of precipitation, topography, and soil depth

Landscape Analysis of Soil Methane Flux Across Complex Terrain

Relationships between methane (CH4) fluxes and environmental conditions have been extensively explored in saturated soils, while research has been less prevalent in aerated soils because of the relatively small magnitudes of CH4 fluxes that occur in dry soils. Our study builds on previous carbon cycle research at Tenderfoot Creek Experimental Forest, Montana, to identify how environmental conditions reflected by topographic metrics can be leveraged to estimate watershed scale CH4 fluxes from point scale measurements. Here, we measured soil CH4 concentrations and fluxes across a range of landscape positions (7 riparian, 25 upland), utilizing topographic and seasonal (29 May–12 September) gradients to examine the relationships between environmental variables, hydrologic dynamics, and CH4 emission and uptake. Riparian areas emitted small fluxes of CH4 throughout the study (median: 0.186 µg CH4–C m−2 h−1) and uplands increased in sink strength with dry-down of the watershed (median: −22.9 µg CH4–C m−2 h−1). Locations with volumetric water content (VWC) below 38 % were methane sinks, and uptake increased with decreasing VWC. Above 43 % VWC, net CH4 efflux occurred, and at intermediate VWC net fluxes were near zero. Riparian sites had near-neutral cumulative seasonal flux, and cumulative uptake of CH4 in the uplands was significantly related to topographic indices. These relationships were used to model the net seasonal CH4 flux of the upper Stringer Creek watershed (−1.75 kg CH4–C ha−1). This spatially distributed estimate was 111 % larger than that obtained by simply extrapolating the mean CH4 flux to the entire watershed area. Our results highlight the importance of quantifying the space–time variability of net CH4 fluxes as predicted by the frequency distribution of landscape positions when assessing watershed scale greenhouse gas balances

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

Abstract. Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology–soil–vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale. </jats:p

Variability in soil respiration across riparian-hillslope transitions

The spatial and temporal controls on soil CO2 production and surface CO2 efflux have been identified as outstanding gaps in our understanding of carbon cycling. We investigated both across two riparian-hillslope transitions in a subalpine catchment, northern Rocky Mountains, Montana. Riparian-hillslope transitions provide ideal locations for investigating the controls on soil CO2 dynamics due to strong, natural gradients in the factors driving respiration, including soil water content (SWC) and soil temperature. We measured soil air CO2 concentrations (20 and 50 cm), surface CO2 efflux, soil temperature, and SWC at eight locations. We investigated (1) how soil CO2 concentrations differed within and between landscape positions; (2) how the timing of peak soil CO2 concentrations varied across riparian and hillslope zones; and (3) whether higher soil CO2 concentrations necessarily resulted in higher efflux (i.e. did surface CO2 efflux follow patterns of subsurface CO2)? Soil CO2 concentrations were significantly higher in the riparian zones, likely due to higher SWC. The timing of peak soil CO2 concentrations also differed between riparian and hillslope zones, with highest hillslope concentrations near peak snowmelt and highest riparian concentrations during the late summer and early fall. Surface CO2 efflux was relatively homogeneous at monthly timescales as a result of different combinations of soil CO2 production and transport, which led to equifinality in efflux across the transects. However, efflux was 57% higher in the riparian zones when integrated to cumulative growing season efflux, and suggests higher riparian soil CO2 production

Welcome to Implementation Science

Implementation research is the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services and care. This relatively new field includes the study of influences on healthcare professional and organisational behaviour. Implementation Science will encompass all aspects of research in this field, in clinical, community and policy contexts. This online journal will provide a unique platform for this type of research and will publish a broad range of articles – study protocols, debate, theoretical and conceptual articles, rigorous evaluations of the process of change, and articles on methodology and rigorously developed tools – that will enhance the development and refinement of implementation research. No one discipline, research design, or paradigm will be favoured. Implementation Science looks forward to receiving manuscripts that facilitate the continued development of the field, and contribute to healthcare policy and practice

Conformational Heterogeneity in a Fully Complementary DNA Three-Way Junction with a GC-Rich Branchpoint.

DNA three-way junctions (3WJs) are branched structures that serve as important biological intermediates and as components in DNA nanostructures. We recently derived the global structure of a fully complementary 3WJ and found that it contained unpaired bases at the branchpoint, which is consistent with previous observations of branch flexibility and branchpoint reactivity. By combining high-resolution single-molecule Förster resonance energy transfer, molecular modeling, time-resolved ensemble fluorescence spectroscopy, and the first (19)F nuclear magnetic resonance observations of fully complementary 3WJs, we now show that the 3WJ structure can adopt multiple distinct conformations depending upon the sequence at the branchpoint. A 3WJ with a GC-rich branchpoint adopts an open conformation with unpaired bases at the branch and at least one additional conformation with an increased number of base interactions at the branchpoint. This structural diversity has implications for branch interactions and processing in vivo and for technological applications

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