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Seismicity in the Los Angeles metropolitan area has been primarily attributed to the regional stress loading. Below the urban areas, earthquake sequences have occurred over time showing migration off the faults and providing evidence that secondary processes may be involved in their evolution. Combining high-frequency seismic attenuation with other geophysical observations is a powerful tool for understanding which Earth properties distinguish regions with ongoing seismicity. We develop the first high-resolution 3D seismic attenuation models across the region east of downtown Los Angeles using 5,600 three-component seismograms from local earthquakes recorded by a dense seismic array. We present frequency-dependent peak delay and coda-attenuation tomography as proxies for seismic scattering and absorption, respectively. The scattering models show high sensitivity to the seismicity along some of the major faults, such as the Cucamonga fault and the San Jacinto fault zone, while a channel of low scattering in the basement extends from near the San Andreas fault westward. In the vicinity of the Fontana seismic sequence, high absorption, low scattering, and seismicity migration across a fault network suggest fluid-driven processes. Our attenuation and fault network imaging characterize near-fault zones and rock-fluid properties beneath the study area for future improvements in seismic hazard evaluation.

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The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (Ziphius cavirostris). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.

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In the United States and abroad, ortho-phthalates and non-ortho-phthalate plasticizers continue to be used within a diverse array of consumer products. Prior California-specific biomonitoring programs for ortho-phthalates have focused on rural, agricultural communities and, to our knowledge, these programs have not measured the potential for exposure to non-ortho-phthalate plasticizers. Therefore, the potential for human exposure to ortho-phthalates and non-ortho-phthalate plasticizers have not been adequately addressed in regions of California that have higher population density. Since there are numerous sources of ortho-phthalates and non-ortho-phthalate plasticizers in population-dense, urban regions, the objective of this study was to leverage silicone wristbands to quantify aggregate ortho-phthalate and non-ortho-phthalate plasticizer exposure over a 5-day period across two different cohorts (2019 and 2020) of undergraduate students at the University of California, Riverside (UCR) that commute from all over Southern California. Based on 5 d of aggregate exposure across two different cohorts, total ortho-phthalate plus non-ortho-phthalate plasticizer concentrations ranged, on average, from ∼100,000-1,000,000 ng/g. Based on the distribution of individual ortho-phthalate and non-ortho-phthalate plasticizer concentrations, the concentrations of di-isononyl phthalate (DiNP, a high molecular weight ortho-phthalate), di (2-ethylhexyl) phthalate (DEHP, a high molecular weight ortho-phthalate), and di-2-ethylhexyl terephthalate (DEHT, a non-ortho-phthalate plasticizer) detected within wristbands were higher than the remaining seven ortho-phthalates and non-ortho-phthalate plasticizers measured, accounting for approximately 94-97% of the total mass depending on the cohort. Overall, our findings raise concerns about chronic DiNP, DEHP, and DEHT exposure in urban, population-dense regions throughout California.

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Hot droughts, droughts attributed to below-average precipitation and exceptional warmth, are increasingly common in the twenty-first century, yet little is known about their effect on coniferous tree growth because of their historical rarity. In much of the American West, including California, radial tree growth is principally driven by precipitation, and narrow ring widths are typically associated with either drier or drought conditions. However, for species growing at high elevations (e.g., Larix lyalli, Pinus albicaulis), growth can be closely aligned with above-average temperatures with maximum growth coinciding with meteorological drought, suggesting that the growth effects of drought span from adverse to beneficial depending on location. Here, we compare radial growth responses of three high-elevation old-growth pines (Pinus jeffreyi, P. lambertiana, and P. contorta) growing in the San Jacinto Mountains, California, during a twenty-first-century hot drought (2000-2020) largely caused by exceptional warmth and a twentieth-century drought (1959-1966) principally driven by precipitation deficits. Mean radial growth during the hot drought was 12% above average while 18% below average during the mid-century drought illustrating that the consequences of environmental stress exhibit spatiotemporal variability. We conclude that the effects of hot droughts on tree growth in high-elevation forests may produce responses different than what is commonly associated with extended dry periods for much of western North America's forested lands at lower elevational ranges and likely applies to other mountainous regions (e.g., Mediterranean Europe) defined by summer-dry conditions. Thus, the climatological/biological interactions discovered in Southern California may offer clues to the unique nature of high-elevation forested ecosystems globally.

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Southern California is a diverse region that is home to a high concentration of food science companies, with an increasing demand for additional food scientists and technologists to join this workforce. Despite this abundance of food science companies and the high demand for jobs, there is currently a shortage in the number of qualified food scientists and technologists in the region. This shortage is also observed within higher education, with declining enrollments in the food science graduate and undergraduate programs across Southern California. Here, we conduct a case study to explore the factors that influence students from Southern California to pursue or not pursue careers in food science. We surveyed both undergraduate and graduate students currently enrolled in food science as well as industry professionals in the region to determine sources of knowledge about the discipline, and motivations and barriers for pursuing careers in food science. We also surveyed high school educators in the region to gain additional perspectives on how food science is being introduced at the secondary level, if at all. Our results demonstrate that many students and high school educators are not knowledgeable about career options within food science and that students who are pursuing food science largely report similar motivations for pursuing the discipline as those currently working in the food science industry. We conclude by discussing implications for the food science education community within Southern California and beyond.

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In the year 1950, Edmund Dubois was asked to evaluate eight patients who had positive results from a new blood test known as the LE cell prep. This was the springboard for him to launch a career that elucidated new and unique insights into the pathogenesis, clinical presentation, laboratory testing, and treatment of systemic lupus erythematosus. Between 1950 and 1985, he treated more than 2000 patients with the disorder and wrote the principal textbook on the subject.

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In the western United States, Culex tarsalis is the most important vector of West Nile virus. Insecticides containing permethrin or other pyrethroid compounds are commonly used to control these mosquitoes. Because of the range of environments where Cx. tarsalis are found, this species is under insecticide pressure from both vector control and agricultural spraying. Mosquito populations may evolve resistance through mechanisms such as target site insensitivity, including the frequently identified knockdown resistance (kdr) mutations. Prevalence of permethrin resistance was determined for Cx. tarsalis from 5 southern California field sites representing 2 distinct valley regions (Coachella Valley and Inland Valley), which are geographically separated by the north-south-running Peninsular Mountain Ranges. These two valley regions are >100 km apart and vary considerably in their environmental and habitat characteristics. Permethrin resistance in mosquito populations was determined by the Centers for Disease Control and Prevention (CDC) bottle bioassay, using glass bottles coated with permethrin at 0.19 µg/cm2 of internal surface. Permethrin resistance was evident in Cx. tarsalis populations from the Coachella Valley field sites with all sites showing similar mortality in the bottle bioassay, while Cx. tarsalis from the Inland Valley field sites were largely susceptible to permethrin, with mortality rates that were similar to a susceptible lab strain of Cx. tarsalis.

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Introduction  As the leading cause of preventable chronic diseases in adults 18 years and older, tobacco usage in the U.S. results in over 20 million premature deaths annually. Current smokers might need extra support on the path to successfully quitting.  Aim To evaluate the influence of predictors of smoking-on-smoking cessation in the Freedom From Tobacco Program (FFT) offered by Southern California Permanente Medical Group (SCPMG). Methods This was a quasi-experimental study to evaluate rates of smoking cessation among participants in the FFT program. There were 471 participants in the study. Factors of the Social Ecological Model (SEM) and demographics were examined to determine if they could predict tobacco cessation. The SEM suggests that an individual's behavior is integrated into a network of intrapersonal characteristics, interpersonal processes, institutional factors, community features, and public policy. In particular, the study mainly addressed the institutional factor. It was promoted within a Health Management Organization and the interpersonal process because it was a group intervention.  Findings  After multiple regression analyses with all predictors from the SEM and demographics, the only significant predictor was the number of previous attempts to quit. Smokers who tried to stop four or more times in the past were 2.6 times (p<0.03) more likely to quit than those who tried fewer times. As we are aware, this was the first time this result was found for programs implemented by Health Management Organizations. The general quit rate at 12 months for the FFT program was 43.1%. Conclusion As the only predictor of quitting in this study was the number of previous attempts to quit smoking, the recommendation is to develop longer-term smoking cessation programs or a longer follow-up to facilitate smokers who relapse to go back and try to quit again. Another recommendation is to identify the main reasons for relapse and try to address these factors in further interventions.

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IMPORTANCE: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data. OBJECTIVE: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

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Benthic sediment toxicity is linked to harmful effects in marine organisms and humans, and an understanding of the link would require, in part, a comprehensive and exhaustive analysis of sediment toxicity data already in hand. One tool which could aid in the process is machine learning (ML), a supervised classification modeling technique that has transformed how actionable insight are acquired from large datasets. The current study is a test of concept in which an ML classifier is sought that can accurately extrapolate the characteristics of a 5437 California-wide coastal training dataset (assembled from 1635 samples) to predict sediment toxicity in southern California bight (SCB). Twelve classifiers were trained to recognize sediment toxicity using 70 % of the dataset and among them, a Gradient Boosting Classifier (GBC) model using latitude, longitude, and water depth was found to be the most accurate at predicting toxicity (83 %). Among the variables, latitude was found to be the most significant driver of prediction by GBC in this test ecosystem. The performance of the model was verified with the remaining 30 % of the dataset and found to be 83 % accurate. Presented with 884 unfamiliar data points assembled from 854 measurements at 346 stations across SCB, GBC was 87 % accurate post-training, thus demonstrating a role supervised learning can play in the southern California environmental analytics.

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Introduction: Talaromyces marneffei causes a systemic fungal infection, referred to as talaromycosis, in immunocompromised individuals. Talaromycosis is an AIDS (acquired immunodeficiency syndrome) defining illness for patients living in the Southeast Asian region. Here we present two rarely reported cases of pulmonary talaromycosis in Southern California in patients with active cancer, negative HIV status, and no prior travel history to endemic regions. Case description: Case 1: A 76-year-old male with a past medical history of emphysema and latent tuberculosis status post rifampin treatment, presented with a necrotic lung mass. He was diagnosed with squamous cell lung carcinoma and bronchoalveolar lavage cultures grew Talaromyces marneffei. He had no animal exposure or prior travel history to Asia. Due to a transfusion reaction to liposomal amphotericin (the mainstay of treatment), he required a transition to posaconazole. He was HIV-negative and expired due to underlying cancer and infection complications.Case2: A 63-year-old male with a past medical history of tuberculosis, diabetes, and cavitary pneumonia with bronchoscopy positive for Talaromyces presented with worsening back pain and was found to have multiple sites of poorly differentiated adenocarcinoma likely originating from gastric adenocarcinoma. He was HIV-negative and expired due to complications from underlying cancer and infection. Conclusion: We demonstrate that patients with pulmonary Talaromyces are becoming more prominent outside of endemic areas even in the setting of no prior travel. In addition, since patients with this infection are severely immunosuppressed, they require extensive workup for other comorbidities such as possible underlying cancer or tuberculosis.

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The global emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has become a critical public healthcare concern due to treatment challenges and high mortality. In recent years, there has been an increase in cases of CRKP co-producing New Delhi metallo-ß-lactamases (NDM) and oxacillinase 48 (OXA-48)-like carbapenemases in the US. The aim of this study was to correlate the clinical and genomic characteristics of CRKP co-producing NDM and OXA-48-like carbapenemases isolated from patients in Southern California since 2016. Whole-genome sequencing was performed on clinical isolates obtained from various sources, including blood, abdominal fluid, wounds, and urine. Genetic diversity was observed in these CRKP, including ST-14, ST-16, ST-167, ST-437, ST-2096, and ST-2497 lineages. Phylogenetic analysis revealed two closely related clusters (ST-14 and ST-2497), with single nucleotide polymorphism (SNP) differences ranging from 0 to 36, suggesting a possible local spread of these CRKP. Significant antimicrobial resistance (AMR) genes were identified in these CRKP, including blaNDM-1, blaNDM-5, blaOXA-232, blaOXA-181, blaCTX-M-15, armA, tet(A), and tet(D). Moreover, pColKP3-type and Inc-type plasmids known to harbor AMR genes were also detected in these isolates. Most of the patients infected with this rare type of CRKP died, although their severe comorbidities also played important roles in their demise. Our study highlighted the extremely limited treatment options and poor clinical outcomes associated with these dual-carbapenemase-producing CRKP. Real-time genomic surveillance of these unusual and deadly CRKP can provide critical information for infection prevention and treatment guidance.

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The isopod crustaceans reported from or expected to occur in littoral and sublittoral marine habitats of the Southern California Bight (SCB) in the northeastern Pacific Ocean are reviewed. A total of 190 species, representing 105 genera in 42 families and six suborders are covered. Approximately 84% of these isopods represent described species with the remaining 16% comprising well-documented "provisional" but undescribed species. Cymothoida and Asellota are the most diverse of the six suborders, accounting for ca. 36% and 29% of the species, respectively. Valvifera and Sphaeromatidea are the next most speciose suborders with between 13-15% of the species each, while the suborder Limnorioidea represents fewer than 2% of the SCB isopod fauna. Finally, the mostly terrestrial suborder Oniscidea accounts for ca. 5% of the species treated herein, each which occurs at or above the high tide mark in intertidal habitats. A key to the suborders and superfamilies is presented followed by nine keys to the SCB species within each of the resultant groups. Figures are provided for most species. Bathymetric range, geographic distribution, type locality, habitat, body size, and a comprehensive list of references are included for most species.

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In this study, we combine machine learning and geospatial interpolations to create a two-dimensional high-resolution ozone concentration fields over the South Coast Air Basin for the entire year of 2020. Three spatial interpolation methods (bicubic, IDW, and ordinary kriging) were employed. The predicted ozone concentration fields were constructed using 15 building sites, and random forest regression was employed to test predictability of 2020 data based on input data from past years. Spatially interpolated ozone concentrations were evaluated at twelve sites that were independent of the actual spatial interpolations to find the most suitable method for SoCAB. Ordinary kriging interpolation had the best performance overall for 2020: concentrations were overestimated for Anaheim, Compton, LA North Main Street, LAX, Rubidoux, and San Gabriel sites and underestimated for Banning, Glendora, Lake Elsinore, and Mira Loma sites. The model performance improved from the West to the East, exhibiting better predictions for inland sites. The model is best at interpolating ozone concentrations inside the sampling region (bounded by the building sites), with R2 ranging from 0.56 to 0.85 for those sites, as prediction deficiencies occurred at the periphery of the sampling region, with the lowest R2 of 0.39 for Winchester. All the interpolation methods poorly predicted and underestimated ozone concentrations in Crestline during summer (up to 19 ppb). Poor performance for Crestline indicates that the site has a distribution air pollution levels independent from all other sites. Therefore, historical data from coastal and inland sites should not be used to predict ozone in Crestline using data-driven spatial interpolation approaches. The study demonstrates the utility of machine learning and geospatial techniques for evaluating air pollution levels during anomalous periods.

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Biomass estimates for shrub-dominated ecosystems in southern California have been generated at national and statewide extents. However, existing data tend to underestimate biomass in shrub vegetation types are limited to one point in time, or estimate aboveground live biomass only. In this study, we extended our previously developed estimates of aboveground live biomass (AGLBM) based on the empirical relationship of plot-based field biomass measurements to Landsat normalized difference vegetation index (NDVI) and multiple environmental factors to include other vegetative pools of biomass. AGLBM estimates were made by extracting plot values from elevation, solar radiation, aspect, slope, soil type, landform, climatic water deficit, evapotranspiration, and precipitation rasters and then using a random forest model to estimate per-pixel AGLBM across our southern California study area. By substituting year-specific Landsat NDVI and precipitation data, we created a stack of annual AGLBM raster layers for each year from 2001 to 2021. Using these AGLBM data as a foundation, we developed decision rules to estimate belowground, standing dead, and litter biomass pools. These rules were based on relationships between AGLBM and the biomass of the other vegetative pools derived primarily from peer-reviewed literature and an existing spatial data set. For shrub vegetation types (our primary focus), rules were based on literature estimates by the postfire regeneration strategy of each species (obligate seeder, facultative seeder, obligate resprouter). Similarly, for nonshrub vegetation types (grasslands, woodlands) we used literature and existing spatial data sets specific to each vegetation type to define rules to estimate the other pools from AGLBM. Using a Python language script that accessed Environmental Systems Research Institute raster geographic information system utilities, we applied decision rules to create raster layers for each of the non-AGLBM pools for the years 2001-2021. The resulting spatial data archive contains a zipped file for each year; each of these files contains four 32-bit tiff files for each of the four biomass pools (AGLBM, standing dead, litter, and belowground). The biomass units are grams per square meter (g/m2 ). We estimated the uncertainty of our biomass data by conducting a Monte Carlo analysis of the inputs used to generate the data. Our Monte Carlo technique used randomly generated values for each of the literature-based and spatial inputs based on their expected distribution. We conducted 200 Monte Carlo iterations, which produced percentage uncertainty values for each of the biomass pools. Results showed, using 2010 as an example, mean biomass for the study area and percentage uncertainty for each of the pools as follows: AGLBM (905.4 g/m2 , 14.4%); standing dead (644.9 g/m2 , 1.3%); litter (731.2 g/m2 , 1.2%); and belowground (776.2 g/m2 , 17.2%). Because our methods are consistently applied across each year, the data produced can be used to inform changes in biomass pools due to disturbance and subsequent recovery. As such, these data provide an important contribution to supporting the management of shrub-dominated ecosystems for monitoring trends in carbon storage and assessing the impacts of wildfire and management activities, such as fuel management and restoration. There are no copyright restrictions on the data set; please cite this paper and the data package when using these data.

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BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

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As habitat generalists, urban coyote (Canis latrans) populations often utilize an abundance of diverse food sources in cities. Within southern California, domestic cats (Felis catus) comprise a higher proportion of coyote diets than in other studied urban areas throughout the United States. However, it is unclear which ecological factors contribute to higher rates of cat depredation by coyotes in this region. While previous research suggests that coyote presence may have a negative effect on free-ranging domestic cat distributions, few studies have determined whether urban green spaces affect coyote or free-ranging domestic cat occurrence and activity within a predominantly urbanized landscape. We placed 20 remote wildlife cameras across a range of green spaces and residential sites in Culver City, California, an area of Los Angeles County experiencing pronounced coyote-domestic cat conflict. Using data collected across 6 months from 2019-2020, we assessed the influence of green space and prey species (i.e., cottontail rabbits (Sylvilagus spp.) and domestic cats) on coyote habitat use and activity. Coyotes exhibited a preference for sites with higher amounts of green space, while domestic cat habitat use was high throughout our study region. Although cottontail rabbit habitat use was also highly associated with urban green space, neither cottontails nor domestic cats appeared to temporally overlap significantly with coyotes. Unlike other cities where coyotes and domestic cats exhibit strong habitat partitioning across the landscape, domestic cats and coyotes spatially overlapped in green space fragments throughout Culver City. We suggest that this pattern of overlap may be responsible for the frequent cases of domestic cat depredation by coyotes in Culver City.

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Habitat loss, flood control infrastructure, and drought have left most of southern California and northern Baja California's native freshwater fish near extinction, including the endangered unarmoured threespine stickleback (Gasterosteus aculeatus williamsoni). This subspecies, an unusual morph lacking the typical lateral bony plates of the G. aculeatus complex, occurs at arid southern latitudes in the eastern Pacific Ocean and survives in only three inland locations. Managers have lacked molecular data to answer basic questions about the ancestry and genetic distinctiveness of unarmoured populations. These data could be used to prioritize conservation efforts. We sampled G. aculeatus from 36 localities and used microsatellites and whole genome data to place unarmoured populations within the broader evolutionary context of G. aculeatus across southern California/northern Baja California. We identified three genetic groups with none consisting solely of unarmoured populations. Unlike G. aculeatus at northern latitudes, where Pleistocene glaciation has produced similar historical demographic profiles across populations, we found markedly different demographics depending on sampling location, with inland unarmoured populations showing steeper population declines and lower heterozygosity compared to low armoured populations in coastal lagoons. One exception involved the only high elevation population in the region, where the demography and alleles of unarmoured fish were similar to low armoured populations near the coast, exposing one of several cases of artificial translocation. Our results suggest that the current "management-by-phenotype" approach, based on lateral plates, is incidentally protecting the most imperilled populations; however, redirecting efforts toward evolutionary units, regardless of phenotype, may more effectively preserve adaptive potential.

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Hypervirulent Klebsiella pneumoniae (hvKp) is more invasive and virulent than classical K. pneumoniae, and requires specialized treatment. To raise clinical awareness, this study determined the prevalence, clinical characteristics, and genomic epidemiology of hvKp infections in Southern California (SoCal) by conducting a passive surveillance in a single large academic medical center. We report here that hvKp infections were more common than expected, accounting for 2.6% of invasive K. pneumoniae infections, and presented with a wide disease spectrum, occasionally mimicking tumors, even co-infecting a COVID-19 patient. Most infections were community acquired with no recent international travel, suggesting hvKp strains are circulating in the community. Genomic analysis revealed genetic diversity, with the K1-ST23 lineage predominating but not clonal, and multiple sequence types of K2 including a SoCal unique K2-ST66 sublineage that had been unrecognized. Our findings highlight the urgency of heightened awareness of hvKp infection in the US, the need for rapid diagnosis of hvKp, and the necessity of implementing robust surveillance programs for hvKp at the institutional or local level.

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Deep-sea images are routinely collected during at-sea expeditions and represent a repository of under-utilized knowledge. We leveraged dive videos collected by the remotely-operated vehicle Hercules (deployed from E/V Nautilus, operated by the Ocean Exploration Trust), and adapted biological trait analysis, to develop an approach that characterizes ecosystem services. Specifically, fisheries and climate-regulating services related to carbon are assessed for three southern California methane seeps: Point Dume (∼725 m), Palos Verdes (∼506 m), and Del Mar (∼1023 m). Our results enable qualitative intra-site comparisons that suggest seep activity influences ecosystem services differentially among sites, and site-to-site comparisons that suggest the Del Mar site provides the highest relative contributions to fisheries and carbon services. This study represents a first step towards ecosystem services characterization and quantification using deep-sea images. The results presented herein are foundational, and continued development should help guide research and management priorities by identifying potential sources of ecosystem services.

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