Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity.

Understanding how background levels of dissolved minerals vary in streams temporally and spatially is needed to assess salinization of fresh water, establish reasonable thresholds and restoration goals, and determine vulnerability to extreme climate events like drought. We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 µS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought.

Propagule pressure and stream characteristics influence introgression: cutthroat and rainbow trout in British Columbia.

Hybridization and introgression between introduced and native salmonids threaten the continued persistence of many inland cutthroat trout species. Environmental models have been developed to predict the spread of introgression, but few studies have assessed the role of propagule pressure. We used an extensive set of fish Stocking records and geographic information system (GIS) data to produce a spatially explicit index of potential propagule pressure exerted by introduced rainbow trout in the Upper Kootenay River, British Columbia, Canada. We then used logistic regression and the information-theoretic approach to test the ability of a set of environmental and spatial variables to predict the level of introgression between native westslope cutthroat trout and introduced rainbow trout. Introgression was assessed using between four and seven co-dominant, diagnostic nuclear markers at 45 sites in 31 different streams. The best model for predicting introgression included our GIS propagule pressure index and an environmental variable that accounted for the biogeoclimatic zone of the site (r2=0.62). This model was 1.4 times more likely to explain introgression than the next-best model, which consisted of only the propagule pressure index variable. We created a composite model based on the model-averaged results of the seven top models that included environmental, spatial, and propagule pressure variables. The propagule pressure index had the highest importance weight (0.995) of all variables tested and was negatively related to sites with no introgression. This study used an index of propagule pressure and demonstrated that propagule pressure had the greatest influence on the level of introgression between a native and introduced trout in a human-induced hybrid zone.

Incorporating functional traits to enhance multimetric index performance and assess land use gradients.

Taxonomic-based multimetric indices (MMIs) have been widely employed for assessing ecosystem status, particularly through the use of stream macroinvertebrate assemblages. However, the functional diversity and composition of assemblages is also important for maintaining stream ecosystem condition. Nonetheless, aquatic insect functional diversity and composition have not commonly been included in MMIs. Our goal was to advance our understanding of the performance and ecological interpretation of an MMI that potentially combined functional and taxonomic metrics. We sampled aquatic insects and natural and land-use variables at 74 temperate Chinese streams. We selected a candidate set of 36 functional and 20 taxonomic metrics that were screened by range tests, natural variation, responsiveness to anthropogenic disturbance, and redundancy for subsequent inclusion in MMIs. We determined if natural variation adjustments improved the performance of a functional-taxonomic MMI. Finally, we evaluated the degree to which the functional-taxonomic MMI served as an early-warning indicator of land use intensity. Natural variation explained between 19.62% and 71.02% of metric variability, indicating that functional metrics changed systematically along natural gradients. The final functional-taxonomic MMI adjusted for natural variation incorporated multiple aspects of assemblage characteristics: functional richness, Rao's quadratic entropy, abundance-weighted frequency of soft bodies, abundance-weighted frequency of predators, and number of Diptera taxa. In contrast to the natural variation unadjusted MMI, the functional-taxonomic adjusted MMI clearly distinguished least-disturbed sites from most-disturbed sites, exhibited high precision and low bias, and showed a significant negative response to land uses. The slope of a linear regression relative to 0-10% urban and 0-20% agriculture was significantly steeper for the functional-taxonomic adjusted MMI than that of the taxonomic adjusted MMI. We conclude that functional-taxonomic adjusted MMIs are more effective indicators of ecological condition and risks to biota from human pressures than are purely taxonomic unadjusted MMIs because functional-taxonomic MMIs are more sensitive to subtle anthropogenic pressures.

Predicting combined effects of land use and climate change on river and stream salinity.

Agricultural, industrial and urban development have all contributed to increased salinity in streams and rivers, but the likely effects of future development and climate change are unknown. I developed two empirical models to estimate how these combined effects might affect salinity by the end of this century (measured as electrical conductivity, EC). The first model predicts natural background from static (e.g. geology and soils) and dynamic (i.e. climate and vegetation) environmental factors and explained 78% of the variation in EC. I then compared the estimated background EC with current measurements at 2001 sites chosen probabilistically from all conterminous USA streams. EC was more than 50% greater at 34% of these sites. The second model predicts deviation of EC from background as a function of human land use and environmental factors and explained 60% of the variation in alteration from background. I then predicted the effects of climate and land use change on EC at the end of the century by replacing dynamic variables with published projections of future conditions based on the A2 emissions scenario. By the end of the century, the median EC is predicted to increase from 0.319 mS cm-1 to 0.524 mS cm-1 with over 50% of streams having greater than 50% increases in EC and 35% more than doubling their EC. Most of the change is related to increases in human land use, with climate change accounting for only 12% of the increase. In extreme cases, increased salinity may make water unsuitable for human use, but widespread moderate increases are likely a greater threat to stream ecosystems owing to the elimination of low EC habitats.This article is part of the theme issue 'Salt in freshwaters: causes, ecological consequences and future prospects'.

Predicting current and future background ion concentrations in German surface water under climate change.

Salinization of surface waters is a global environmental issue that can pose a regional risk to freshwater organisms, potentially leading to high environmental and economic costs. Global environmental change including climate and land use change can increase the transport of ions into surface waters. We fit both multiple linear regression (LR) and random forest (RF) models on a large spatial dataset to predict Ca2+ (266 sites), Mg2+ (266 sites), and [Formula: see text] (357 sites) ion concentrations as well as electrical conductivity (EC-a proxy for total dissolved solids with 410 sites) in German running water bodies. Predictions in both types of models were driven by the major factors controlling salinity including geologic and soil properties, climate, vegetation and topography. The predictive power of the two types of models was very similar, with RF explaining 71-76% of the spatial variation in ion concentrations and LR explaining 70-75% of the variance. Mean squared errors for predictions were all smaller than 0.06. The factors most strongly associated with stream ion concentrations varied among models but rock chemistry and climate were the most dominant. The RF model was subsequently used to forecast the changes in EC that were likely to occur for the period of 2070 to 2100 in response to just climate change-i.e. no additional effects of other anthropogenic activities. The future forecasting shows approximately 10% and 15% increases in mean EC for representative concentration pathways 2.6 and 8.5 (RCP2.6 and RCP8.5) scenarios, respectively.This article is part of the theme issue 'Salt in freshwaters: causes, ecological consequences and future prospects'.

Connecting Earth observation to high-throughput biodiversity data.

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

A Rasch model analysis of technology usage in Minnesota hospitals.

PURPOSE: To identify whether the level of difficulty varied among various healthcare technologies. In addition, to examine the whether the degree of healthcare technology adoption was related to patient safety. METHODS: The data on healthcare technology usage came from a survey of hospitals in Minnesota. There were responses from individuals within 104 hospitals for an effective response rate of 72.7%. The data on patient safety was taken from the Hospital Compare database. Rasch model analysis and regression analysis were used to analyze the data. RESULTS: Rasch model analysis revealed that the difficulty of implementation of healthcare information technologies varied by the particular technology. That is, some technologies were more difficult than other technologies. Further, it was found that the degree of healthcare information technology implementation within a hospital was significantly related to patient safety. CONCLUSION: This study identified design and policy implications for hospital decision makers. In particular, it was shown that the technological capability of a hospital is a key consideration in determining the level of resources that are necessary to implement specific healthcare technologies within a hospital.

Examining quality improvement programs: the case of Minnesota hospitals.

OBJECTIVE: To determine if there is a hierarchy of improvement program adoption by hospitals and outline that hierarchy. DATA SOURCES: Primary data were collected in the spring of 2007 via e-survey from 210 individuals representing 109 Minnesota hospitals. Secondary data from 2006 were assembled from the Leapfrog database. STUDY DESIGN: As part of a larger survey, respondents were given a list of improvement programs and asked to identify those programs that are used in their hospital. DATA COLLECTION/DATA EXTRACTION: Rasch Model Analysis was used to assess whether a unidimensional construct exists that defines a hospital's ability to implement performance improvement programs. Linear regression analysis was used to assess the relationship of the Rasch ability scores with Leapfrog Safe Practices Scores to validate the research findings. Principal Findings. The results of the study show that hospitals have widely varying abilities in implementing improvement programs. In addition, improvement programs present differing levels of difficulty for hospitals trying to implement them. Our findings also indicate that the ability to adopt improvement programs is important to the overall performance of hospitals. CONCLUSIONS: There is a hierarchy of improvement programs in the health care context. A hospital's ability to successfully adopt improvement programs is a function of its existing capabilities. As a hospital's capability increases, the ability to successfully implement higher level programs also increases.