Indexing and partitioning the spatial linear model for large data sets

We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different challenges when analyzing large spatial data sets. Current research uses a variety of methods, including spatial basis functions (reduced rank), covariance tapering, etc, to achieve these goals. However, spatial indexing, which is very similar to composite likelihood, offers some advantages. We develop a simple framework for all four goals listed above by using indexing to create a block covariance structure and nearest-neighbor predictions while maintaining a coherent linear model. We show exact inference for fixed effects under this block covariance construction. Spatial indexing is very fast, and simulations are used to validate methods and compare to another popular method. We study various sample designs for indexing and our simulations showed that indexing leading to spatially compact partitions are best over a range of sample sizes, autocorrelation values, and generating processes. Partitions can be kept small, on the order of 50 samples per partition. We use nearest-neighbors for kriging and block kriging, finding that 50 nearest-neighbors is sufficient. In all cases, confidence intervals for fixed effects, and prediction intervals for (block) kriging, have appropriate coverage. Some advantages of spatial indexing are that it is available for any valid covariance matrix, can take advantage of parallel computing, and easily extends to non-Euclidean topologies, such as stream networks. We use stream networks to show how spatial indexing can achieve all four goals, listed above, for very large data sets, in a matter of minutes, rather than days, for an example data set.

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Supporting conscious competency: Validation of the Generic Supervision Assessment Tool (GSAT)

OBJECTIVES

Clinical supervision is essential for ensuring effective service delivery. International imperatives to demonstrate professional competence has increased attention on the role of supervision in enhancing client outcomes. Although supervisor competency tools are recognised as important components in effective supervision, there remains a shortage of tools that are evidenced-based, applicable across workforces and freely accessible.

DESIGN

An expert multidisciplinary group developed the Generic Supervision Assessment Tool (GSAT) to assess supervisor competencies across a range of professions. Initially the GSAT consisted of 32 items responded to by either a supervisor (GSAT-SR) or supervisee (GSAT-SE). The current study, using surveys, employed a cross-sectional design to test the reliability and construct validity of the GSAT.

METHODS

The study consisted of two phases and included 12 professional groups across Australasia. In 2018, exploratory factor analysis (EFA) was undertaken with survey data from 479 supervisors and 447 supervisees. In 2019 survey data from 182 supervisors and 186 supervisees were used to conduct confirmatory factor analysis (CFA). The results were used to refine and validate the GSAT.

RESULTS

The final GSAT-SR has four factors with 26 competency items. The final GSAT-SE has two factors with 21 competency items. The EFA and CFA confirmed that the GSAT-SR and the GSAT-SE are psychometrically valid tools that supervisors and supervisees can utilise to assess competencies.

CONCLUSION

As a non-discipline specific supervision tool, the GSAT is a validated, freely available tool for benchmarking the competencies of clinical supervisors across professions, potentially optimising supervisory evaluation processes and strengthening supervision effectiveness.

PRACTITIONER POINTS

Supervisor competency tools are recognised as important components of safe and effective supervision provision yet there is a dearth of valid, reliable and effective measures. The Generic Supervision Assessment Tool (GSAT-SR and GSAT-SE) are unique psychometrically valid, and reliable measures of supervisor competence. The GSAT-SR and the GSAT-SE can enhance translation of evidence-based supervision competency skills into regular practice. Validated with a broad cross section of professionals in diverse practice settings the GSAT provides a comprehensive conceptualization of supervisor competence.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Connecting virtual reality and ecology: a new tool to run seamless immersive experiments in R

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences. We present R2VR, a package for implementing and performing VR experiments in R with the aim of easing the learning curve for applied scientists including ecologists. The package provides functions for rendering VR scenes on web browsers with A-Frame that can be viewed by multiple users on smartphones, laptops, and VR headsets. It also provides instructions on how to retrieve answers from an online database in R. Three published ecological case studies are used to illustrate the R2VR workflow, and show how to run a VR experiments and collect the resulting datasets. By tapping into the popularity of R among ecologists, the R2VR package creates new opportunities to address the complex challenges associated with conservation, improve scientific knowledge, and promote new ways to share better understanding of environmental issues. The package could also be used in other fields outside of ecology.

Ion Heating and Flow Driven by an Instability Found in Plasma Couette Flow

We present the first observation of instability in weakly magnetized, pressure dominated plasma Couette flow firmly in the Hall regime. Strong Hall currents couple to a low frequency electromagnetic mode that is driven by high-β (>1) pressure profiles. Spectroscopic measurements show heating (factor of 3) of the cold, unmagnetized ions via a resonant Landau damping process. A linear theory of this instability is derived that predicts positive growth rates at finite β and shows the stabilizing effect of very large β, in line with observations.

Laboratory Resolved Structure of Supercritical Perpendicular Shocks

Supermagnetosonic perpendicular flows are magnetically driven by a large radius theta-pinch experiment. Fine spatial resolution and macroscopic coverage allow the full structure of the plasma-piston coupling to be resolved in laboratory experiment for the first time. A moving ambipolar potential is observed to reflect unmagnetized ions to twice the piston speed. Magnetized electrons balance the radial potential via Hall currents and generate signature quadrupolar magnetic fields. Electron heating in the reflected ion foot is adiabatic.

The Natural Killer-Dendritic Cell Immune Axis in Anti-Cancer Immunity and Immunotherapy

Natural killer (NK) cells and dendritic cells (DCs) are crucial mediators of productive immune responses to infection and disease. NK cells and a subtype of DCs, the type 1 conventional DCs (cDC1s), are individually important for regulating immune responses to cancer in mice and humans. Recent work has found that NK cells and cDC1s engage in intercellular cross-talk integral to initiating and coordinating adaptive immunity to cancer. This NK cell-cDC1 axis has been linked to increased overall survival and responses to anti-PD-1 immunotherapy in metastatic melanoma patients. Here, we review recent findings on the role of NK cells and cDC1s in protective immune responses to cancer and immunotherapy, as well as current therapies targeting this NK cell-cDC1 axis. Further, we explore the concept that intercellular cross-talk between NK cells and cDC1s may be key for many of the positive prognostic associations seen with NK cells and DCs individually. It is clear that increasing our understanding of the NK cell-cDC1 innate immune cell axis will be critical for the generation of novel therapies that can modulate anti-cancer immunity and increase patient responses to common immunotherapies.

A stochastic model of jaguar abundance in the Peruvian Amazon under climate variation scenarios

The jaguar (Panthera onca) is the dominant predator in Central and South America, but is now considered near-threatened. Estimating jaguar population size is difficult, due to uncertainty in the underlying dynamical processes as well as highly variable and sparse data. We develop a stochastic temporal model of jaguar abundance in the Peruvian Amazon, taking into account prey availability, under various climate change scenarios. The model is calibrated against existing data sets and an elicitation study in Pacaya Samiria. In order to account for uncertainty and variability, we construct a population of models over four key parameters, namely three scaling parameters for aquatic, small land, and large land animals and a hunting index. We then use this population of models to construct probabilistic evaluations of jaguar populations under various climate change scenarios characterized by increasingly severe flood and drought events and discuss the implications on jaguar numbers. Results imply that jaguar populations exhibit some robustness to extreme drought and flood, but that repeated exposure to these events over short periods can result in rapid decline. However, jaguar numbers could return to stability-albeit at lower numbers-if there are periods of benign climate patterns and other relevant factors are conducive.

Weakly Magnetized, Hall Dominated Plasma Couette Flow

A novel plasma equilibrium in the high-β, Hall regime that produces centrally peaked, high Mach number Couette flow is described. Flow is driven using a weak, uniform magnetic field and large, cross field currents. Large magnetic field amplification (factor 20) due to the Hall effect is observed when electrons are flowing radially inward, and near perfect field expulsion is observed when the flow is reversed. A dynamic equilibrium is reached between the amplified (removed) field and extended density gradients.

SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

Representation and complementarity of the long-term coral monitoring on the Great Barrier Reef

Effective environmental management hinges on efficient and targeted monitoring, which in turn should adapt to increasing disturbance regimes that now characterize most ecosystems. Habitats and biodiversity of Australia's Great Barrier Reef (GBR), the world's largest coral reef ecosystem, are in declining condition, prompting a review of the effectiveness of existing coral monitoring programs. Applying a regional model of coral cover (i.e., the most widely used proxy for coral reef condition globally) within major benthic communities, we assess the representation and complementarity of existing long-term coral reef monitoring programs on the GBR. We show that existing monitoring has captured up to 45% of the environmental diversity on the GBR, while some geographic areas (including major hotspots of cyclone activity over the last 30 yr) have remained unmonitored. Further, we identified complementary groups of reefs characterized by similar benthic community composition and similar coral cover trajectories since 1996. The mosaic of their distribution across the GBR reflects spatial variation in the cumulative impact of multiple acute disturbances, as well as spatial gradients in coral recovery potential. Representation and complementarity, in combination with other performance assessment criteria, can inform the cost-effective design and stratification of future surveys. Based on these results, we formulate recommendations to assist with the design of future long-term coral reef monitoring programs.

Forecasting intensifying disturbance effects on coral reefs

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal.

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Predicting sediment and nutrient concentrations from high-frequency water-quality data

Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Giant coral reef fishes display markedly different susceptibility to night spearfishing

The humphead wrasse (Cheilinus undulatus) and bumphead parrotfish (Bolbometopon muricatum) are two of the largest, most iconic fishes of Indo-Pacific coral reefs. Both species form prized components of subsistence and commercial fisheries and are vulnerable to overfishing. C. undulatus is listed as Endangered and B. muricatum as Vulnerable on the IUCN Red List of Threatened Species. We investigated how night spearfishing pressure and habitat associations affected both species in a relatively lightly exploited setting; the Kia fishing grounds, Isabel Province, Solomon Islands. We used fisheries-independent data from underwater visual census surveys and negative binomial models to estimate abundances of adult C. undulatus and B. muricatum as a function of spearfishing pressure and reef strata. Our results showed that, in Kia, night spearfishing pressure from free divers had no measurable effect on C. undulatus abundances, but abundances of B. muricatum were 3.6 times lower in areas of high spearfishing pressure, after accounting for natural variations due to habitat preferences. It is likely the species' different nocturnal aggregation behaviors, combined with the fishers' use of night spearfishing by spot-checking underpin these species' varying susceptibility. Our study highlights that B. muricatum is extremely susceptible to night spearfishing; however, we do not intend to draw conservation attention away from C. undulatus. Our data relate only to the Kia fishing grounds, where human population density is low, the spot-checking strategy is effective for reliably spearing large numbers of fish, particularly B. muricatum, and fisheries have only recently begun to be commercialized; such conditions are increasingly rare. Instead, we recommend that regional managers assess the state of their fisheries and the dynamics affecting the vulnerability of the fishes to fishing pressure based on local-scale, fisheries-independent data, where resources permit.

Using virtual reality to estimate aesthetic values of coral reefs

Aesthetic value, or beauty, is important to the relationship between humans and natural environments and is, therefore, a fundamental socio-economic attribute of conservation alongside other ecosystem services. However, beauty is difficult to quantify and is not estimated well using traditional approaches to monitoring coral-reef aesthetics. To improve the estimation of ecosystem aesthetic values, we developed and implemented a novel framework used to quantify features of coral-reef aesthetics based on people's perceptions of beauty. Three observer groups with different experience to reef environments (Marine Scientist, Experienced Diver and Citizen) were virtually immersed in Australian's Great Barrier Reef (GBR) using 360° images. Perceptions of beauty and observations were used to assess the importance of eight potential attributes of reef-aesthetic value. Among these, heterogeneity, defined by structural complexity and colour diversity, was positively associated with coral-reef-aesthetic values. There were no group-level differences in the way the observer groups perceived reef aesthetics suggesting that past experiences with coral reefs do not necessarily influence the perception of beauty by the observer. The framework developed here provides a generic tool to help identify indicators of aesthetic value applicable to a wide variety of natural systems. The ability to estimate aesthetic values robustly adds an important dimension to the holistic conservation of the GBR, coral reefs worldwide and other natural ecosystems.

An experiment on the dynamics of ion implantation and sputtering of surfaces

A major impediment towards a better understanding of the complex plasma-surface interaction is the limited diagnostic access to the material surface while it is undergoing plasma exposure. The Dynamics of ION Implantation and Sputtering Of Surfaces (DIONISOS) experiment overcomes this limitation by uniquely combining powerful, non-perturbing ion beam analysis techniques with a steady-state helicon plasma exposure chamber, allowing for real-time, depth-resolved in situ measurements of material compositions during plasma exposure. Design solutions are described that provide compatibility between the ion beam analysis requirements in the presence of a high-intensity helicon plasma. The three primary ion beam analysis techniques, Rutherford backscattering spectroscopy, elastic recoil detection, and nuclear reaction analysis, are successfully implemented on targets during plasma exposure in DIONISOS. These techniques measure parameters of interest for plasma-material interactions such as erosion/deposition rates of materials and the concentration of plasma fuel species in the material surface.

Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority in many regions, there is an increasing interest in the scale at which land use influences stream ecosystem health. Our goal was to use a substantial data set collected as part of a monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program data set, collected at 116 sites over six years) to identify the spatial scale of land use, or the combination of spatial scales, that most strongly influences overall ecosystem health. In addition, we aimed to determine whether the most influential scale differed for different aspects of ecosystem health. We used linear-mixed models and a Bayesian model-averaging approach to generate models for the overall aggregated ecosystem health score and for each of the five component indicators (fish, macroinvertebrates, water quality, nutrients, and ecosystem processes) that make up the score. Dense forest close to the survey site, mid-dense forest in the hydrologically active near-stream areas of the catchment, urbanization in the riparian buffer, and tree cover at the reach scale were all significant in explaining ecosystem health, suggesting an overriding influence of forest cover, particularly close to the stream. Season and antecedent rainfall were also important explanatory variables, with some land-use variables showing significant seasonal interactions. There were also differential influences of land use for each of the component indicators. Our approach is useful given that restoring general ecosystem health is the focus of many stream restoration projects; it allowed us to predict the scale and catchment position of restoration that would result in the greatest improvement of ecosystem health in the regions streams and rivers. The models we generated suggested that good ecosystem health can be maintained in catchments where 80% of hydrologically active areas in close proximity to the stream have mid-dense forest cover and moderate health can be obtained with 60% cover.

Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species-juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)-in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69-95%, 51-87%, and 86-100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network

Mountain streams provide important habitats for many species, but their faunas are especially vulnerable to climate change because of ectothermic physiologies and movements that are constrained to linear networks that are easily fragmented. Effectively conserving biodiversity in these systems requires accurate downscaling of climatic trends to local habitat conditions, but downscaling is difficult in complex terrains given diverse microclimates and mediation of stream heat budgets by local conditions. We compiled a stream temperature database (n = 780) for a 2500-km river network in central Idaho to assess possible trends in summer temperatures and thermal habitat for two native salmonid species from 1993 to 2006. New spatial statistical models that account for network topology were parameterized with these data and explained 93% and 86% of the variation in mean stream temperatures and maximas, respectively. During our study period, basin average mean stream temperatures increased by 0.38 degrees C (0.27 degrees C/decade), and maximas increased by 0.48 degrees C (0.34 degrees C/decade), primarily due to long-term (30-50 year) trends in air temperatures and stream flows. Radiation increases from wildfires accounted for 9% of basin-scale temperature increases, despite burning 14% of the basin. Within wildfire perimeters, however, stream temperature increases were 2-3 times greater than basin averages, and radiation gains accounted for 50% of warming. Thermal habitat for rainbow trout (Oncorhynchus mykiss) was minimally affected by temperature increases, except for small shifts towards higher elevations. Bull trout (Salvelinus confluentus), in contrast, were estimated to have lost 11-20% (8-16%/decade) of the headwater stream lengths that were cold enough for spawning and early juvenile rearing, with the largest losses occurring in the coldest habitats. Our results suggest that a warming climate has begun to affect thermal conditions in streams and that impacts to biota will be specific to both species and context. Where species are at risk, conservation actions should be guided based on considerations of restoration opportunity and future climatic effects. To refine predictions based on thermal effects, more work is needed to understand mechanisms associated with biological responses, climate effects on other habitat features, and habitat configurations that confer population resilience.

A mixed-model moving-average approach to geostatistical modeling in stream networks

Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where nested watersheds and flow connectivity may produce patterns that are not captured by Euclidean distance. Yet, many common autocovariance functions used in geostatistical models are statistically invalid when Euclidean distance is replaced with hydrologic distance. We use simple worked examples to illustrate a recently developed moving-average approach used to construct two types of valid autocovariance models that are based on hydrologic distances. These models were designed to represent the spatial configuration, longitudinal connectivity, discharge, and flow direction in a stream network. They also exhibit a different covariance structure than Euclidean models and represent a true difference in the way that spatial relationships are represented. Nevertheless, the multi-scale complexities of stream environments may not be fully captured using a model based on one covariance structure. We advocate using a variance component approach, which allows a mixture of autocovariance models (Euclidean and stream models) to be incorporated into a single geostatistical model. As an example, we fit and compare "mixed models," based on multiple covariance structures, for a biological indicator. The mixed model proves to be a flexible approach because many sources of information can be incorporated into a single model.

Patterns of spatial autocorrelation in stream water chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.

Predicting water quality impaired stream segments using landscape-scale data and a regional geostatistical model: a case study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r(2) = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.

Prevalence of hypertrophic cardiomyopathy in an outpatient population referred for echocardiographic study

Although hypertrophic cardiomyopathy (HC) is believed to be a relatively uncommon cardiac disease, the frequency with which it occurs in the general or cardiac population has not been defined. To address this issue, the patient population of a community-based echocardiography laboratory was used to assess the prevalence of HC in 714 consecutively studied outpatients with (or suspected of having) heart disease. The most common cardiac disease identified was mitral valve prolapse (73 patients [10%]), and HC was present in 4 patients (0.5%). Ages were 50 to 69 years. Maximal left ventricular wall thicknessess were 15 to 22 mm (mean 19); only 1 had evidence of obstruction to left ventricular outflow by virtue of marked mitral valve systolic anterior motion. Before echocardiographic study, each of the 4 patients with HC had signs or symptoms of cardiac disease, but the correct diagnosis had not been suspected. Of 11 other patients who were referred for echocardiographic study because of a clinical suspicion of HC, none proved to have this disease. The present study demonstrates that HC is a particularly uncommon disease entity occurring in about 0.5% of an unselected outpatient population referred for echocardiographic study.

Coexistence of mitral valve prolapse in a consecutive group of 528 patients with hypertrophic cardiomyopathy assessed with echocardiography

Hypertrophic cardiomyopathy and mitral valve prolapse are both conditions that may be genetically transmitted and incur a risk for sudden cardiac death. Although the small left ventricular cavity and distorted geometry characteristic of hypertrophic cardiomyopathy might suggest a predisposition to mitral valve prolapse, the frequency with which these two entities coexist and the potential clinical significance of such an association are not known. To further define the relation of hypertrophic cardiomyopathy and mitral valve prolapse, 528 consecutive patients with hypertrophic cardiomyopathy were studied by echocardiography. Patients ranged in age from 1 to 86 years (mean 45); 335 (63%) were male. Unequivocal echocardiographic evidence of systolic mitral valve prolapse into the left atrium was identified in only 16 (3%) of the 528 patients. The mitral valve excised at operation from three of the patients had morphologic characteristics of a floppy mitral valve, which was judged to be responsible for the echocardiographic findings. Occurrence of clinically evident atrial fibrillation was common in patients with hypertrophic cardiomyopathy and mitral valve prolapse (9 [56%] of 16). Hence, in a large group of patients with hypertrophic cardiomyopathy, the association of echocardiographically documented mitral valve prolapse was uncommon. The coexistence of mitral valve prolapse in patients with hypertrophic cardiomyopathy appears to predispose such patients to atrial fibrillation.