Performance of Different Crop Models in Simulating Soil Temperature.

Soil temperature is one of the key factors to be considered in precision agriculture to increase crop production. This study is designed to compare the effectiveness of a land surface model (Noah Multiparameterization (Noah-MP)) against a traditional crop model (Environmental Policy Integrated Climate Model (EPIC)) in estimating soil temperature. A sets of soil temperature estimates, including three different EPIC simulations (i.e., using different parameterizations) and a Noah-MP simulations, is compared to ground-based measurements from across the Central Valley in California, USA, during 2000-2019. The main conclusion is that relying only on one set of model estimates may not be optimal. Furthermore, by combining different model simulations, i.e., by taking the mean of two model simulations to reconstruct a new set of soil temperature estimates, it is possible to improve the performance of the single model in terms of different statistical metrics against the reference ground observations. Containing ratio (CR), Euclidean distance (dist), and correlation co-efficient (R) calculated for the reconstructed mean improved by 52%, 58%, and 10%, respectively, compared to both model estimates. Thus, the reconstructed mean estimates are shown to be more capable of capturing soil temperature variations under different soil characteristics and across different geographical conditions when compared to the parent model simulations.

Quantifying drought's influence on moist soil seed vegetation in California's Central Valley through remote sensing.

California's Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 2007 to 2017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley, we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n = 68, R2  = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha (29,845-34,893 ha 95% CI), and 13,012 ha (11,628-14,396 ha) for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffe's test, P < 0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

Cross-scale controls on the in-stream dynamics of nitrate and turbidity in semiarid agricultural waterway networks.

Stream and riparian zone networks embedded in agricultural landscapes provide a potential intervention point to ameliorate the negative effects of agricultural runoff by reducing transport of nitrate (NO3-) and suspended sediments (SS) downstream. However, our ability to support and promote NO3- and SS attenuation is limited by our understanding of vegetative and hydrogeomorphic controls in realistic management contexts. In addition, agricultural landscapes are heterogenous on multiple management scales, from farm field to regional water management scales, and the effect of these heterogeneities and how they interact across scales to affect vegetative and hydrogeomorphic controls is poorly explored in many settings. This is especially true in irrigated agricultural settings, where stream and riparian networks are entwined with and sensitive to water management systems. To fill these gaps, we related the vegetative and hydrogeomorphic features of 67 waterway reaches across two water management districts in the California Central Valley to reach-scale NO3- and turbidity attenuation and district-scale water quality patterns. We found that in-stream NO3- attenuation was rare, but, when it did occur, it was promoted by shallow and wide riparian banks, low flows, and high channel-edge denitrification potential. Nitrate concentrations were consistently higher in upstream reaches compared to water district outlets, suggesting that while exports from the district were low, agricultural runoff may impair within-district water resources. Turbidity attenuation was highly variable and unrelated to vegetative or hydrogeomorphic features, suggesting that onfield controls are crucial to managing suspended sediments. We conclude that waterway networks have the potential to mitigate the effects of agricultural NO3- runoff in this setting, but that more effective monitoring and adoption of NO3- attenuating features is needed. Using our findings, we make specific management and monitoring recommendations at both reach and water district scales.

Integrating Genetic and Demographic Effects of Connectivity on Population Stability: The Case of Hatchery Trucking in Salmon.

Connectivity among populations can have counteracting effects on population stability. Demographically, connectivity can rescue local populations but increase the synchrony across populations. Genetically, connectivity can counteract drift locally but homogenize genotypes across populations. Population independence and diversity underlies system-level buffering against environmental variability, termed the portfolio effect. The portfolio effect has declined in California fall-run Chinook salmon, possibly in part because of the trucking of juvenile hatchery-reared fish for downstream release, which reduces juvenile mortality but increases the connectivity between rivers. We use a dynamical population model to test whether this increased connectivity can explain the loss of the portfolio effect and quantify the relative demographic and genetic contributions to portfolio effect erosion. In the model, populations experience different within-population environmental conditions and the same time-variable ocean conditions, the response to which can depend on a quantitative genetic trait. We find that increased trucking for one population's hatchery can lead to a loss of the portfolio effect, with a system-level trade-off between increased average abundance and increased variability in abundance. This trade-off is much stronger when we include the effects of genetic homogenization than when we consider demographic synchronization alone. Therefore, genetic homogenization can outweigh demographic synchrony in determining the system-level effect of connectivity.

Quantifying shorebird habitat in managed wetlands by modeling shallow water depth dynamics.

Over 50% of Western Hemisphere shorebird species are in decline due to ongoing habitat loss and degradation. In some regions of high wetland loss, shorebirds are heavily reliant on a core network of remaining human-managed wetlands during migration journeys in the spring and fall. While most refuges have been designed and managed to match the habitat needs of waterfowl, shorebirds typically require much shallower water (<10 cm deep). Traditional static habitat modeling approaches at relatively coarse spatial and temporal resolution are insufficient to capture dynamic changes within this narrow water depth range. Our objectives were to (1) develop a method to quantify shallow water habitat distributions in inland non-tidal wetlands, and (2) to assess how water management practices affect the amount of shorebird habitat in Sacramento National Wildlife Refuge Complex. We produced water depth distributions and modeled optimal habitat (<10 cm deep) within 23 managed wetlands using high-resolution topography and fixed-point water depth records. We also demonstrated that habitat availability, specifically suitable water depth ranges, can be tracked from satellite imagery and high-resolution topography. We found that wetlands with lower topographic roughness may have a higher potential to provide shorebird habitat and that strategically reducing water levels could increase habitat extent. Over 50% of the wetlands measured provided optimal habitat across <10% of their area at the peak of migration in early April, and most provided a brief duration of shallow water habitat. Reducing water volumes could increase the proportion of optimal habitat by 1-1,678% (mean = 294%) compared to actual volumes measured at peak spring migration in 2016. For wetlands with a high habitat potential, beginning wetland drawdown earlier and extending drawdown time could dramatically improve habitat conditions at the peak of shorebird migration. Our approach can be adapted to track dynamic hydrologic changes at broader spatial scales as additional high-resolution topographic (e.g., lidar, drone imagery photogrammetry) and optical remote sensing data (e.g., planet imagery, drone photography) become available.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California.

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area - despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system.

Population-level genetic variation and climate change in a biodiversity hotspot.

INTRODUCTION: Estimated future climate scenarios can be used to predict where hotspots of endemism may occur over the next century, but life history, ecological and genetic traits will be important in informing the varying responses within myriad taxa. Essential to predicting the consequences of climate change to individual species will be an understanding of the factors that drive genetic structure within and among populations. Here, I review the factors that influence the genetic structure of plant species in California, but are applicable elsewhere; existing levels of genetic variation, life history and ecological characteristics will affect the ability of an individual taxon to persist in the presence of anthropogenic change. FACTORS INFLUENCING THE DISTRIBUTION OF GENETIC VARIATION: Persistence in the face of climate change is likely determined by life history characteristics: dispersal ability, generation time, reproductive ability, degree of habitat specialization, plant-insect interactions, existing genetic diversity and availability of habitat or migration corridors. Existing levels of genetic diversity in plant populations vary based on a number of evolutionary scenarios that include endemism, expansion since the last glacial maximum, breeding system and current range sizes. REGIONAL PRIORITIES AND EXAMPLES: A number of well-documented examples are provided from the California Floristic Province. Some predictions can be made for the responses of plant taxa to rapid environmental changes based on geographic position, evolutionary history, existing genetic variation, and ecological amplitude. CONCLUSIONS, SOLUTIONS AND RECOMMENDATIONS: The prediction of how species will respond to climate change will require a synthesis drawing from population genetics, geography, palaeontology and ecology. The important integration of the historical factors that have shaped the distribution and existing genetic structure of California's plant taxa will enable us to predict and prioritize the conservation of species and areas most likely to be impacted by rapid climate change, human disturbance and invasive species.

Remote sensing of temperature-dependent mosquito and viral traits predicts field surveillance-based disease risk.

Mosquito-borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito-borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait-based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing-based approach to mapping temperature-dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature-dependent traits and field-based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6-25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait-based mechanistic models. This study represents one of the highest resolution validations of trait-based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito-borne disease risk.

Grapevine bacterial communities display compartment-specific dynamics over space and time within the Central Valley of California.

BACKGROUND: Plant organs (compartments) host distinct microbiota which shift in response to variation in both development and climate. Grapevines are woody perennial crops that are clonally propagated and cultivated across vast geographic areas, and as such, their microbial communities may also reflect site-specific influences. These site-specific influences along with microbial differences across sites compose 'terroir', the environmental influence on wine produced in a given region. Commercial grapevines are typically composed of a genetically distinct root (rootstock) grafted to a shoot system (scion) which adds an additional layer of complexity via genome-to-genome interactions. RESULTS: To understand spatial and temporal patterns of bacterial diversity in grafted grapevines, we used 16S rRNA amplicon sequencing to quantify soil and compartment microbiota (berries, leaves, and roots) for grafted grapevines in commercial vineyards across three counties in the Central Valley of California over two successive growing seasons. Community composition revealed compartment-specific dynamics. Roots assembled site-specific bacterial communities that reflected rootstock genotype and environment influences, whereas bacterial communities of leaves and berries displayed associations with time. CONCLUSIONS: These results provide further evidence of a microbial terroir within the grapevine root systems but also reveal that the microbiota of above-ground compartments are only weakly associated with the local soil microbiome in the Central Valley of California.

A new species of alkali-sink Paruroctonus Werner, 1934 (Scorpiones, Vaejovidae) from California's San Joaquin Valley.

Herein a new species of Paruroctonus Werner, 1934 is described from alkali-sink habitats in the San Joaquin Desert of central California, Paruroctonustularesp. nov. It can be differentiated from other Paruroctonus by a combination of morphological features including scalloped pedipalp fingers in males, specific setal counts and morphometric ratios, and specific patterns of fuscous pigmentation. It also inhabits a unique distribution allopatric with all other Paruroctonus species except P.variabilis Hjelle, 1982. Photographs of a large series of live P.tularesp. nov. from across their range and detailed images of several morphological features are provided, their distribution is modeled, a haplotype network is presented, and details about their habitat, ecology, and conservation are discussed.

Host Correlates of Avian Influenza Virus Infection in Wild Waterfowl of the Sacramento Valley, California.

Avian influenza viruses (AIVs) are distributed globally in members of the family Anatidae (waterfowl), and significant disease may occur when these viruses infect commercial poultry or humans. Early detection of AIV through surveillance of wild waterfowl is one measure to prevent future disease outbreaks. Surveillance efforts that are designed to account for host and environmental determinants of susceptibility to infection are likely to be most effective. However, these determinants have not been clearly delineated and may vary with location. Because some regions are at greater risk for AIV outbreaks, the factors that contribute to AIV infection of waterfowl in these areas are of interest. We investigated the prevalence of AIVs in hunter-killed waterfowl at wintering sites in California's Central Valley. Overall, AIV prevalence was 10.5% and, after controlling for age and sex, was greatest in northern shovelers (Spatula clypeata) and lowest in wood ducks (Aix sponsa). Overall, AIV prevalence was higher in females than in males, but this trend was driven by one sampling year and one waterfowl species (green-winged teal, Anas crecca). AIV prevalence in waterfowl was lower in samples collected from brackish wetlands compared with those collected from freshwater wetlands, suggesting that wetland type or other environmental factors contribute to AIV prevalence. This study adds to our understanding of the ecology of AIV infection in waterfowl and may assist in developing more efficient, targeted surveillance efforts for the detection of potentially harmful viruses circulating in North American waterfowl.

Correlación de hospedadores en la infección por el virus de la influenza aviar en aves acuáticas silvestres del Valle de Sacramento en California. Los virus de la influenza aviar se distribuyen globalmente en miembros de la familia Anatidae (aves acuáticas) y pueden ocurrir enfermedades importantes cuando estos virus infectan aves comerciales o a los humanos. La detección temprana de los virus de influenza mediante la vigilancia de aves acuáticas silvestres es una medida para prevenir futuros brotes de enfermedades. Es probable que los esfuerzos de vigilancia diseñados para tener en cuenta los determinantes ambientales y del huésped para la susceptibilidad a la infección sean más eficaces. Sin embargo, estos determinantes no se han delineado claramente y pueden variar según la ubicación. Debido a que algunas regiones tienen un mayor riesgo de brotes de influenza aviar, los factores que contribuyen a la infección de las aves acuáticas en estas áreas son de interés. Se investigó la prevalencia de virus de influenza en aves acuáticas muertas por cazadores en sitios de estancia invernal en el Valle Central de California. En general, la prevalencia de los virus de influenza fue del 10.5% y, después de controlar por edad y sexo, fue mayor en los patos cuchara comunes del norte (Spatula clypeata) y más baja en los patos joyuyo (Aix sponsa). En general, la prevalencia de influenza fue mayor en las hembras que en los machos, pero esta tendencia fue influenciada por un año de muestreo y una especie de ave acuática (cerceta común, Anas crecca). La prevalencia de influenza aviar en aves acuáticas fue menor en las muestras recolectadas de humedales salobres en comparación con las recolectadas de humedales de agua dulce, lo que sugiere que el tipo de humedal u otros factores ambientales contribuyen a la prevalencia de los virus de influenza. Este estudio contribuye al conocimiento de la ecología en la infección por influenza aviar en las aves acuáticas y puede ayudar a desarrollar esfuerzos de vigilancia más eficientes y específicos para la detección de virus potencialmente dañinos que circulan en las aves acuáticas de América del Norte.

Quantification of thermal impacts across freshwater life stages to improve temperature management for anadromous salmonids.

Water temperature is the major controlling factor that shapes the physiology, behaviour and, ultimately, survival of aquatic ectotherms. Here we examine temperature effects on the survival of Chinook salmon (Oncorhynchus tshawytscha), a species of high economic and conservation importance. We implement a framework to assess how incremental changes in temperature impact survival across populations that is based on thermal performance models for three freshwater life stages of Chinook salmon. These temperature-dependent models were combined with local spatial distribution and phenology data to translate spatial-temporal stream temperature data into maps of life stage-specific physiological performance in space and time. Specifically, we converted temperature-dependent performance (i.e. energy used by pre-spawned adults, mortality of incubating embryos and juvenile growth rate) into a common currency that measures survival in order to compare thermal effects across life stages. Based on temperature data from two abnormally warm and dry years for three managed rivers in the Central Valley, California, temperature-dependent mortality during pre-spawning holding was higher than embryonic mortality or juvenile mortality prior to smolting. However, we found that local phenology and spatial distribution helped to mitigate negative thermal impacts. In a theoretical application, we showed that high temperatures may inhibit successful reintroduction of threatened Central Valley spring-run Chinook salmon to two rivers where they have been extirpated. To increase Chinook salmon population sizes, especially for the threatened and declining spring-run, our results indicate that adults may need more cold-water holding habitat than currently available in order to reduce pre-spawning mortality stemming from high temperatures. To conclude, our framework is an effective way to calculate thermal impacts on multiple salmonid populations and life stages within a river over time, providing local managers the information to minimize negative thermal impacts on salmonid populations, particularly important during years when cold-water resources are scarce.

Disparities in access for melanoma screening by region, specialty, and insurance: A cross-sectional audit study.

Background: Early detection of melanoma is critical for positive outcomes. However, access for the diagnosis of melanoma remains problematic for segments of the general population. Objective: To compare the rates of dermatology and family medicine practitioner acceptances for a public insurance (Medicaid) versus private insurance (Anthem Blue Cross) and clinic wait times for an appointment for a changing pigmented skin lesion concerning melanoma in rural and urban regions in California. Methods: Cross-sectional audit study between June 2017 and March 2019; scripted phone calls were made to dermatology and family medicine practices (FMPs). Results: Family medicine and dermatology practices in both regions had significantly decreased acceptance of Medicaid. Dermatology practices had 11.3% to 13.0% Medicaid acceptance rates that were less than FMP rates of 28% to 36%. In both regions, FMP wait times were 2.4- to 3.2-fold longer for public versus private insurance; there were little differences in wait times for the 2 insurance types in dermatology practices, in both regions. Limitations: Assessment of only 2 regions in the state of California. Conclusion: Delays at FMPs and insurance types limit access to melanoma screening in California for underserved segments of the general population, which has implications for melanoma outcomes and health policy.

Seasonal and Long-Term Groundwater Unloading in the Central Valley Modifies Crustal Stress.

Changes in terrestrial water content cause elastic deformation of the Earth's crust. This deformation is thought to play a role in modulating crustal stress and seismicity in regions where large water storage fluctuations occur. Groundwater is an important component of total water storage change in California, helping to drive annual water storage fluctuations and loss during periods of drought. Here we use direct estimates of groundwater volume loss during the 2007-2010 drought in California's Central Valley obtained from high resolution Interferometric Synthetic Aperture Radar-based vertical land motion data to investigate the effect of groundwater volume change on the evolution of the stress field. We show that GPS-derived elastic load models may not capture the contribution of groundwater to terrestrial water loading, resulting in an underestimation of nontectonic crustal stress change. We find that groundwater unloading during the drought causes Coulomb stress change of up to 5.5 kPa and seasonal fluctuations of up to 2.6 kPa at seismogenic depth. We find that faults near the Valley show the largest stress change and the San Andreas fault experiences only ~40 Pa of Coulomb stress change over the course of a year from groundwater storage change. Annual Coulomb stress change peaks dominantly in the fall, when the groundwater level is low; however, some faults experience peak stress in the spring when groundwater levels are higher. Additionally, we find that periods of increased stress correlate with higher than average seismic moment release but are not correlated with an increase in the number of earthquakes. This indicates groundwater loading likely contributes to nontectonic loading of faults, especially near the Valley edge, but is not a dominant factor in modulation of seismicity in California because the amplitude of stress change declines rapidly with distance from the Valley. By carefully quantifying and spatially locating groundwater fluctuations, we will improve our understanding of what drives nontectonic stress and forces that modulate seismicity in California.

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California's almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

Prognostic value of frontal QRS-T angle in predicting survival after primary percutaneous coronary revascularization/coronary artery bypass grafting for ST-elevation myocardial infarction.

BACKGROUND: Frontal QRS-T angle (FQRST) has previously been correlated with mortality in patients with stable coronary artery disease, but its role as survival predictor after ST-elevation myocardial infarction (STEMI) remains unknown. METHODS: We evaluated 267 consecutive patients with STEMI undergoing reperfusion or coronary artery bypass grafting. Data assessed included demographics, clinical presentation, electrocardiograms, medical therapy, and one-year mortality. RESULTS: Of 267 patients, 187 (70%) were males and most (49.4%) patients were Caucasian. All-cause mortality was significantly higher among patients with the highest (101-180°) FQRST [28% vs. 15%, p = 0.02]. Patients with FQRST 1-50° had higher survival (85.6%) compared with FQRST = 51-100° (72.3%) and FQRST = 101-180° (67.9%), [log rank, p = 0.01]. Adjusting for significant variables identified during univariate analysis, FQRST (OR = 2.04 [95% CI: 1.31-13.50]) remained an independent predictor of one-year mortality. FQRST-based risk score (1-50° = 0 points, 51-100° = 2 points, 101-180° = 5 points) had excellent discriminatory ability for one-year mortality when combined with Mayo Clinic Risk Score (C statistic = 0.875 [95%CI: 0.813-0.937]. A high (>4 points) FQRST risk score was associated with greater mortality (32% vs. 19%, p = 0.02) and longer length of stay (6 vs. 2 days, p < 0.001). CONCLUSION: FQRST represents a novel independent predictor of one-year mortality in patients with STEMI undergoing reperfusion. A high FQRST-based risk score was associated with greater mortality and longer length of stay and, after combining with Mayo Clinic Risk Score, improved discriminatory ability for one-year mortality.

Impact of extreme drought and incentive programs on flooded agriculture and wetlands in California's Central Valley.

BACKGROUND: Between 2013 and 2015, a large part of the western United States, including the Central Valley of California, sustained an extreme drought. The Central Valley is recognized as a region of hemispheric importance for waterbirds, which use flooded agriculture and wetlands as habitat. Thus, the impact of drought on the distribution of surface water needed to be assessed to understand the effects on waterbird habitat availability. METHODS: We used remote sensing data to quantify the impact of the recent extreme drought on the timing and extent of waterbird habitat during the non-breeding season (July-May) by examining open water in agriculture (rice, corn, and other crops) and managed wetlands across the Central Valley. We assessed the influence of habitat incentive programs, particularly The Nature Conservancy's BirdReturns and The Natural Resources Conservation Service's Waterbird Habitat Enhancement Program (WHEP), at offsetting habitat loss related to drought. RESULTS: Overall, we found statistically significant declines in open water in post-harvest agriculture (45-80% declines) and in managed wetlands (39-60% declines) during the 2013-2015 drought compared to non-drought years during the period of 2000-2011. Crops associated with the San Joaquin Basin, specifically corn, as well as wetlands in that part of the Central Valley exhibited larger reductions in open water than rice and wetlands in the Sacramento Valley. Semi-permanent wetlands on protected lands had significantly lower (39-49%) open water in the drought years than those on non-protected lands while seasonal wetlands on protected lands had higher amounts of open water. A large fraction of the daily open water in rice during certain times of the year, particularly in the fall for BirdReturns (61%) and the winter for WHEP (100%), may have been provided through incentive programs which underscores the contribution of these programs. However, further assessment is needed to know how much the incentive programs directly offset the impact of drought in post-harvest rice by influencing water management or simply supplemented funding for activities that might have been done regardless. DISCUSSION: Our landscape analysis documents the significant impacts of the recent extreme drought on freshwater wetland habitats in the Central Valley, the benefits of incentive programs, and the value of using satellite data to track surface water and waterbird habitats. More research is needed to understand subsequent impacts on the freshwater dependent species that rely on these systems and how incentive programs can most strategically support vulnerable species during future extreme drought.

Quantitative genetic analysis of anxiety trait in bipolar disorder.

BACKGROUND: Bipolar disorder type I (BPI) affects approximately 1% of the world population. Although genetic influences on bipolar disorder are well established, identification of genes that predispose to the illness has been difficult. Most genetic studies are based on categorical diagnosis. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders. METHODS: We studied 619 individuals, 568 participants from 61 extended families and 51 unrelated healthy controls. The sample was 55% female and had a mean age of 43.25 (SD 13.90; range 18-78). Heritability and genetic correlation of the trait scale from the Anxiety State and Trait Inventory (STAI) was computed by using the general linear model (SOLAR package software). RESULTS: we observed that anxiety trait meets the following criteria for an endophenotype of bipolar disorder type I (BPI): 1) association with BPI (individuals with BPI showed the highest trait score (F = 15.20 [5,24], p = 0.009), 2) state-independence confirmed after conducting a test-retest in 321 subjects, 3) co-segregation within families 4) heritability of 0.70 (SE: 0.060), p = 2.33 × 10-14 and 5) genetic correlation with BPI was 0.20, (SE = 0.17, p = 3.12 × 10-5). LIMITATIONS: Confounding factors such as comorbid disorders and pharmacological treatment could affect the clinical relationship between BPI and anxiety trait. Further research is needed to evaluate if anxiety traits are specially related to BPI in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness. CONCLUSIONS: Anxiety trait is a heritable phenotype that follows a normal distribution when measured not only in subjects with BPI but also in unrelated healthy controls. It could be used as an endophenotype in BPI for the identification of genomic regions with susceptibility genes for this disorder.

An ethnographic study of salt use and humoral concepts in a Latino farm worker community in California's Central Valley.

BACKGROUND: This article reports on the use of domestic or table salt for its perceived health effects and healing properties in a Latino farmworker community. It explores how contemporary salt usage beliefs can be seen to have roots in long-standing humoral theories of medicine and health. METHODS: This qualitative investigation comprised 30 in-depth individual interviews and five focus groups conducted in Spanish with Mexican and Central American immigrants in one small city in California's Central Valley (N = 61 total participants). Interviews and focus groups were audiotaped, translated into English and transcribed. Several researchers independently and iteratively read transcripts, developed and applied codes, and engaged in thematic analysis. RESULTS: Strongly emergent themes identified the importance of balance in health, and beliefs about the effects on salt on health. Valued for its culinary role, for bringing out the flavors in food, and used by people of all ages, salt use is part of a robust set of cultural practices. Salt was regularly mixed with foods in different combinations and ingested to restore balance, prevent disequilibrium or reduce vulnerability to diverse illnesses, promote rehydration, and address symptoms of exposure to extremes of temperature or physical or emotional stress. Statements made and practices engaged in by participants were highly suggestive of health and healing beliefs common to humoral belief systems based primarily on a hot-cold dichotomy in classifications of foods and healing behaviors. We evaluate these statements and practices in the context of the existing literature on historical and contemporary humoral beliefs in Latin American communities, in Mexico and Central America, and in the United States. CONCLUSION: Humoral theory is a useful framework for understanding contemporary rural Latino migrant farmworkers' perceptions of the importance of salt for their health.

Sequencing improves our ability to study threatened migratory species: Genetic population assignment in California's Central Valley Chinook salmon.

Effective conservation and management of migratory species requires accurate identification of unique populations, even as they mix along their migratory corridors. While telemetry has historically been used to study migratory animal movement and habitat use patterns, genomic tools are emerging as a superior alternative in many ways, allowing large-scale application at reduced costs. Here, we demonstrate the usefulness of genomic resources for identifying single-nucleotide polymorphisms (SNPs) that allow fast and accurate identification of the imperiled Chinook salmon in the Great Central Valley of California. We show that 80 well-chosen loci, drawn from a pool of over 11,500 SNPs developed from restriction site-associated DNA sequencing, can accurately identify Chinook salmon runs and select populations within run. No other SNP panel for Central Valley Chinook salmon has been able to achieve the high accuracy of assignment we show here. This panel will greatly improve our ability to study and manage this ecologically, economically, and socially important species and demonstrates the great utility of using genomics to study migratory species.