Locations In California Research Articles

Foraging ecology of southern sea otters at the northern range extent informs regional population dynamics

Sea otters Enhydra lutris are vital keystone predators throughout the North Pacific that were nearly extirpated during the maritime fur trade. Recovery of southern sea otters E. l. nereis has proceeded slowly, with much of their historical range remaining unoccupied, resulting in reduced ecosystem functioning. Numerous studies have used foraging metrics to assess the population status of southern sea otters throughout their current range, but little is known about the northern range extent, where a stall in expansion has limited recovery. Thus, we collected census and foraging data of sea otters at Año Nuevo State Park, California, from 2019 to 2021 to determine sea otter abundance, diet composition, diet diversity, and average energy intake rate at the northern range edge. We then assessed regional population status by comparing values from Año Nuevo with previously collected data from other locations in California, including high-density, range center sites and low-density, range periphery sites. We found that sea otter density at Año Nuevo was greater than surrounding areas at the northern range periphery, and the average (±95% CI) energy intake (9.51 ± 0.91 kcal min-1) more closely resembled values observed at high-density sites. Further, dietary diversity (using the Shannon-Wiener index, H) was intermediate between previously studied high- and low-density populations (H = 1.81), with crabs making up the largest proportion of the diet (~56%). Overall, this study highlights possible effects of occupation time and range stagnation, identifies unique aspects of the prey resource base at Año Nuevo, and provides insight into the ongoing lack of northern range expansion.

A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series

A novel approach to Bayesian nonparametric spectral analysis of stationary multivariate time series is presented. Starting with a parametric vector-autoregressive model, the parametric likelihood is nonparametrically adjusted in the frequency domain to account for potential deviations from parametric assumptions. A proof of mutual contiguity of the nonparametrically corrected likelihood, the multivariate Whittle likelihood approximation and the exact likelihood for Gaussian time series is given. A multivariate extension of the nonparametric Bernstein-Dirichlet process prior for univariate spectral densities to the space of Hermitian positive definite spectral density matrices is specified directly on the correction matrices. An infinite series representation of this prior is then used to develop a Markov chain Monte Carlo algorithm to sample from the posterior distribution. The code is made publicly available for ease of use and reproducibility. With this novel approach, a generalisation of the multivariate Whittle-likelihood-based method of Meier et al. (2020) as well as an extension of the nonparametrically corrected likelihood for univariate stationary time series of Kirch et al. (2019) to the multivariate case is presented. It is demonstrated that the nonparametrically corrected likelihood combines the efficiencies of a parametric with the robustness of a nonparametric model. Its numerical accuracy is illustrated in a comprehensive simulation study. Its practical advantages are illustrated by a spectral analysis of two environmental time series data sets: a bivariate time series of the Southern Oscillation Index and fish recruitment and a multivariate time series of windspeed data at six locations in California.

Techno-economic analysis and optimal sizing of hybrid PV-wind systems for hydrogen production by PEM electrolysis in California and Northern Germany

The aim of this study is to assess the economic potentials of power and hydrogen generation via solar and wind energy resources at locations in Northern Germany and California, both of these (geographical) regions being pioneers in terms of a sustainable energy transition. Based on extensive research and electrolyzer manufacturer interviews, green hydrogen generation plants are modeled in a MATLAB® environment. All relevant inputs and outputs of the systems studied (wind power plants, photovoltaic power plants, and polymer electrolyte membrane electrolyzers, among others) are considered for different locations and framework conditions. In contrast to the existing literature, special attention is paid to the part-load behavior of electrolysis systems, which becomes particularly relevant in their interplay with volatile renewables. Metrics such as the levelized costs of electricity (LCOE) of the wind and photovoltaic power plants and the resulting levelized costs of hydrogen (LCOHy) are determined. With the help of the developed model, dimensioning of a whole system's components can be determined for different locations. A case study is conducted for a Northern German site and another one for a site in the Californian Mojave Desert. Both the optimal dimensioning of the renewable energy plants and the ratio of installed wind and photovoltaic power plant capacity are strongly location-dependent. In Northern Germany, for example, lower LCOHy can be generated at higher wind power plant capacity shares and, in the Mojave Desert, be produced at higher photovoltaic power plant capacity shares. In general, with larger plants and correspondingly larger polymer electrolyte membrane electrolyzers, LCOHy are lower due to scaling effects. Following this dimensioning recommendation, the LCOHy vary between 4.5 €/kg and 5.2 €/kg in the Northern German case study and between 4.6 US$/kg and 5.3 US$/kg in the Californian one. With costs of 1–2 €/kg, gray hydrogen is still economically superior.

Effect of landscape irrigation regulations on runoff volume from low-density residential catchments during two California droughts

The severe drought in California (2012–2016) generated significant public and government concern. State and local watering regulations were enacted to reduce residential and commercial water-use during the droughts. This study presents a comparison of residential runoff volumes before and after local landscape irrigation regulations were enacted during the droughts of 2008 and 2012–2016. Each sampling site (Folsom 1 and Folsom 2) was a storm drain outfall that drained a low-density residential catchment in the City of Folsom. Dry season runoff measured at the sampling sites represents neighborhood outdoor water waste, mainly from landscape irrigation. During the drought of 2012–2016, median runoff flows were significantly reduced after local landscape irrigation regulations were enacted. The daily runoff pattern was also highly influenced by regulation, with reductions of daily peak runoff flows on 4–5 days in a week after watering regulations were enacted. The number of peak flow events in the daily runoff pattern were reduced during this period. In addition, a significant reduction in mean runoff volume occurred. Based on these results, the watering regulations enacted by the City of Folsom had a positive effect on reducing urban runoff from residential neighborhoods during the dry season. As the results are from monitoring sites in a relatively small geographical area, further work should evaluate reductions in irrigation runoff from other California locations to determine if this is a localized phenomenon.

Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data.

Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations. We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act. Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.

Abstract 12956: Novel Digital Strategy for Improving Diversity in Cardiovascular Clinical Trials

Background: Diversity of trial participants is crucial for the generalizability of clinical trial findings. Cardiovascular trials often underrepresent non-white participants, women, and older individuals. Investigators for the Apple Heart Study (AHS) and the Project Baseline Health Study (PBHS) used a digital platform or ‘waiting room’ to invite interested individuals in the United States to learn about the study, sign an electronic consent, and provide some baseline information including assessment of key inclusion/exclusion criteria. For AHS, the direct-to-participant approach led to the recruitment of participants that were comparably aligned with the US census. For PBHS, participants were enrolled from a ‘waiting room’ using specific parameters to meet the pre-specified target of mimicking the US census for race and ethnicity. Methods: We describe the sex, age, and race/ethnicity of the population enrolled in AHS and PBHS. AHS was a completely siteless/virtual trial, while PBHS did traditional assessments at physical locations while also having virtual components. Results: A total of 419,297 participants were enrolled in just 8 months in the AHS and 2502 were enrolled in PBHS. The sex, age, and race/ethnicity of the study populations are shown (see Table). 42-55% were women, 11.6% were Hispanic, 8-16% were black, 6-10% were Asian and 63-68% were white. AHS enrolled from all 50 states, while PBHS enrolled from 4 locations in California and North Carolina. Conclusion: In two direct-to-participant clinical trials, we were able to recruit participants that were comparably aligned with the US census. The digital strategies used were successful in both siteless and traditional site-based research studies. The direct-to-participant recruitment approach and digital waiting room are potentially scalable methods for digital recruitment and enrollment to improve diversity in pivotal cardiovascular studies.

A probabilistic reasoning approach to analyze the severity of single-vehicle crashes at mid-ramp locations

Freeway ramps are one of the roadway elements that are considered as crash-prone sites with relatively more crashes per mile than other freeway segments. Among other crash types that occurred on freeway ramps, single-vehicle crashes have been found to be more severe. Thus, understanding the factors influencing the severity of single-vehicle crashes on freeway ramps is essential in improving the safety of our limited-access facilities. This study adopted a discrete Bayesian network (BN) approach to explore the probabilistic relationship among the potential factors associated with the severity of single-vehicle crashes at mid-ramp locations. The analysis was based on 6 041 single-vehicle crashes that occurred at the mid-ramp locations in California from 2009 to 2017. The findings indicated that ramp type, ramp traffic volume, road surface condition, and time of day were directly associated with the severity of single-vehicle crashes at the mid-ramp locations. The interdependency of off-ramps, ramp AADT of less than 13 000 vehicles per day, dry road surface condition, and off-peak hours were associated with the highest risk of fatal/severe injury crashes involving a single-vehicle. The study findings could potentially be used by transportation agencies in planning and implementing several strategies to improve the safety of freeway ramps.

Effects of genotype and environment on productivity and quality in Californian malting barley

AbstractMalting barley (Hordeum vulgare L.) productivity and grain quality are of critical importance to the malting and brewing industry. In this study, we analyzed 12 malting barley genotypes across 8 locations in California and 3 years (2017–2018, 2018–2019, and 2020–2021). The effects of genotype (G), location (L), year (Y), and their interactions were assessed on grain yield (kg ha−1), grain protein content (%), individual‐grain weight (mg), thousand kernel weight (TKW; g), grain size (plump and thin; %), onset gelatinization temperature (GT; temperature at which starch starts to gelatinize), peak GT, offset GT, difference between onset and peak GT, and difference between peak and offset GT. L, Y, and their interaction explained the largest variance for all traits except TKW, peak GT, and difference between onset and peak GT, for which G explained the largest variance. Yield and plump (%) were weakly negatively correlated with onset and peak GT (Pearson's r of −0.15 to −0.21) but showed a positive correlation with the difference between peak and offset GT (Pearson's r of 0.37 and 0.36). The 2020–2021 samples formed partially distinct clusters in principal component analysis, mainly discriminated by high percentage of thins and high onset GT. These findings illustrate the key roles of G, L, and Y in determining malting barley productivity and quality.

Mapping potentially groundwater-dependent vegetation in the Mediterranean biome using global geodata targeting site conditions and vegetation characteristics

Groundwater-dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat for species, and sustaining human livelihoods. However, climate and land-use change are threatening GDV, highlighting the need for harmonised, global mapping of the distribution and extent of GDV. This need is particularly crucial in vulnerable biodiversity hotspots such as the Mediterranean biome. This study presents a novel multicriteria index to identify areas in the Mediterranean biome that provide suitable environmental conditions to support potentially groundwater-dependent vegetation (pGDV) where vegetation behaviour is also indicative of groundwater use. Global datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks have been combined for the first time in an easy-to-use index. Layer weightings from Analytical Hierarchy Process and Random Forest showed limited applicability on biome scale, but an unweighted overlay of eleven thematic layers produced plausible results. The final pGDV map indicates that 31 % of the natural vegetation in the Mediterranean biome likely depend on groundwater. Moreover, moderate to good agreement was found compared to actual GDV locations in Campania, Italy (91 % with at least moderate potential) and California, USA (87 % with at least moderate potential). The results provide valuable information for identifying regions with a substantial presence of pGDV in the Mediterranean biome and can be used for decision making, e.g. to prioritise field surveys and high-resolution remote sensing for GDV mapping. It can therefore support effective groundwater resource management and the conservation of biodiversity hotspots.

Can Forward Commodity Markets Improve Spot Market Performance? Evidence from Wholesale Electricity

Forward markets are believed to aggregate information about future spot prices and reduce the cost of producing the commodity. We develop a measure of the extent to which forward and spot prices agree in markets with transaction costs. Using this measure, we show that day-ahead prices better reflect real-time prices at all locations in California’s electricity market after the introduction of financial trading. We then present evidence suggesting that operating costs and input fuel use fell after the introduction of financial trading on days when the nonconvexities inherent to the production and transmission of electricity are especially relevant. (JEL D23, D24, G13, L94, L98, Q48)

Optimizing Managed Aquifer Recharge Locations in California's Central Valley Using an Evolutionary Multi‐Objective Genetic Algorithm Coupled With a Hydrological Simulation Model

AbstractManaged aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km3. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km3. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost.

On the uncertainty of estimating photovoltaic soiling using nearby soiling data

The accumulation of dust on the surface of photovoltaic modules can reduce their performance and affect the cost competitiveness of this technology. This phenomenon is known as soiling and can be mitigated through appropriate corrective and/or preventive actions. In order to maximize its effectiveness, it is important to plan the soiling mitigation strategy even before the PV system is operational. This is typically done through a nearest neighbor approach, by estimating soiling using data from the nearest operational photovoltaic system. This work focuses on understanding the uncertainty related to this practice. For this purpose, the semi-variance function is used to study the dissimilarity between the soiling losses of two locations in California depending on their distance. The results show that, when the soiling loss at a nearby system is used to estimate soiling of a site, the uncertainty can be approximated to increase linearly at a rate of 0.08–0.10%/km up to 60 or 80 km. After this distance, the use of a nearest neighbor approach is no longer justified, as it produces an uncertainty as big as the average soiling loss of the sites in the dataset used in this study. In some conditions, uncertainties > 0% are found also for sites located within 25 km, meaning that even close-by systems might soil differently.

Dual-Toxin ("Bivalent") Infant Botulism in California, 1976-2020: Epidemiologic, Clinical, and Laboratory Aspects.

To assess the consequences of infant botulismthat result from Clostridium botulinum strains that produce 2 botulinum toxin serotypes, termed "bivalent." Epidemiologic investigations used a standard questionnaire. Clostridium botulinum strains were isolated by standard methods. Botulinum neurotoxin (BoNT) serotypes and the relative amounts of toxins produced were identified using the standard mouse bioassay. BoNT subtypes and genomic locations were identified by DNA nucleotide sequencing. Thirty bivalent cases of infant botulism occurred in the 45years (1976-2020), representing 2.0% of all California infant botulism cases, in the 3 geographic regions of southern California, the southern Central Valley, and mid-northern California. Toxin serotype combinations were Ba (n=22), Bf (n=7), and Ab (n=1). More patients with illness caused by bivalent C botulinum Ba and Bf strains needed endotracheal intubation at hospital admission, 60.0% (18/30), than did patients with illness caused by monovalent BoNT/B strains, 34.3% (152/443). The Cbotulinum Ba and Bf strains produced BoNT/B5 and either BoNT/A4 or /F2. The Ab strain produced BoNT/A2 and /B1. All toxin gene clusters were on plasmids. Infant botulism caused by bivalent Cbotulinum strains occurs sporadically and in diverse locations in California. Affected patients with bivalent Ba and Bf strains lacked distinguishing epidemiological features but appeared to be more severely paralyzed at hospital presentation than patients with illness caused by only BoNT/B. These bivalent strains produced BoNT subtypes A2, A4, B1, B5, and F2, and all toxin gene clusters were on plasmids.

Putting infection on the map: Using heatmaps to characterise within- and between-host distributions of trematode metacercariae.

The location of parasites within individual hosts is often treated as a static trait, yet many parasite species can occur in multiple locations or organs within their hosts. Here, we apply distributional heat maps to study the within- and between-host infection patterns for four trematodes (Alaria marcianae, Cephalogonimus americanus, Echinostoma spp. and Ribeiroia ondatrae) within the amphibian hosts Pseudacris regilla and two species of Taricha. We developed heatmaps from 71 individual hosts from six locations in California, which illustrate stark differences among parasites both in their primary locations within amphibian hosts as well as their degree of location specificity. While metacercariae (i.e., cysts) of two parasites (C. americanus and A. marcianae) were relative generalists in habitat selection and often occurred throughout the host, two others (R. ondatrae and Echinostoma spp.) were highly localised to a specific organ or organ system. Comparing parasite distributions among these parasite taxa highlighted locations of overlap showing potential areas of interactions, such as the mandibular inner dermis region, chest and throat inner dermis and the tail reabsorption outer epidermis. Additionally, the within-host distribution of R. ondatrae differed between host species, with metacercariae aggregating in the anterior dermis areas of newts, compared with the posterior dermis area in frogs. The ability to measure fine-scale changes or alterations in parasite distributions has the potential to provide further insight about ecological questions concerning habitat preference, resource selection, host pathology and disease control.

16.4 The Allcove Youth Mental Health Imperative: Developing Early Intervention Mental Health Programs by and With Young People

Youth in the United States are in the midst of a devastating public mental health crisis. The headspace model from Australia was the first stand-alone, integrated care program for young people to access early mental health, physical health, and school/education support. The headspace model, and other international models that followed, were proven effective to improve young people’s mental, social, and emotional well-being. This presentation will explain the US development of allcove, an innovative integrated health system for young people aged 12 to 25 years, based on headspace, currently established in several locations in California.

Fast Bayesian inference on spectral analysis of multivariate stationary time series

Spectral analysis discovers trends, periodic and other characteristics of a time series by representing these features in the frequency domain. However, when multivariate time series are considered, and the number of components increases, the size of the spectral density matrix grows quadratically, making estimation and inference rather challenging. The proposed novel Bayesian framework considers a Whittle likelihood-based spectral modeling approach and imposes a discounted regularized horseshoe prior on the coefficients that define a spline representation of each of the components of a Cholesky factorization of the inverse spectral density matrix. The proposed prior structure leads to a model that provides higher posterior accuracy when compared to alternative currently available approaches. To achieve fast inference that takes advantage of the massive power of modern hardware (e.g., GPU), a stochastic gradient variational Bayes approach is proposed for the highly parallelizable posterior inference that provides computational flexibility for modeling high-dimensional time series. The accurate empirical performance of the proposed method is illustrated via extensive simulation studies and the analysis of two datasets: a wind speed data from 6 locations in California, and a 61-channel electroencephalogram data recorded on two contrasting subjects under specific experimental conditions.

Atmospheric River Reconnaissance 2021: A Review

Abstract Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days. The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership. Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

Aftershocks Preferentially Occur in Previously Active Areas

Abstract The clearest statistical signal in aftershock locations is that most aftershocks occur close to their mainshocks. More precisely, aftershocks are triggered at distances following a power-law decay in distance (Felzer and Brodsky, 2006). This distance decay kernel is used in epidemic-type aftershock sequence (ETAS) modeling and is typically assumed to be isotropic, even though individual sequences show more clustered aftershock occurrence. The assumption of spatially isotropic triggering kernels can impact the estimation of ETAS parameters themselves, such as biasing the magnitude-productivity term, alpha, and assigning too much weight to secondary rather than primary (direct) triggering. Here we show that aftershock locations in southern California, at all mainshock–aftershock distances, preferentially occur in the areas of previous seismicity. For a given sequence, the scaling between aftershock rates and the previous seismicity rate is approximately linear. However, the total number of aftershocks observed for a given sequence is independent of background rate. We explain both of these observations within the framework of rate-and-state friction (Dieterich, 1994).

First Report of Strains Within the Pythium spinosum Species Complex Causing Carrot Cavity Spot in California.

Carrots (Daucus carota L. subsp. sativus (Hoffm.)) with typical symptoms of cavity spot, i.e., sunken, round to elliptical lesions of 2-5 mm long (Hiltunen and White 2002), were collected from two locations in California, Bakersfield and Riverside, in January and July 2019, respectively. Carrots were rinsed in tap water, 4-mm2 lesion fragments were pressed into selective corn meal agar (CMA-PARP; Schrandt et al. 1994) and incubated at 23ºC in the dark for four days. Identification of pure cultures was performed via amplification and sequence analysis of two genomic regions, the Internal Transcribed Spacer 1-5.8S-ITS2 (ITS) region and the cytochrome C oxidase subunit 1 (COI) gene, using the universal primers UN-UP18S42/UN-LO28S576B (Schroeder et al. 2006) and OomCOXI-Levup/OomCOXI-Levlo (Robideau et al. 2011), respectively. Via BLAST, two isolates from organically grown carrots in Bakersfield (MCIF19) and Riverside (JSCS19), with identical ITS sequences (GenBank Acc. Nos. MZ799354 and MZ799355, respectively), showed 99.61% similarity (1021/1025 bp) to that of Pythium spinosum (AY598701.2). Yet, the COI of MCIF19 (MZ803207) showed 98.72% similarity (692/701 bp) to that of Pythium paroecandrum (GU071818.1), while the COI of JSCS19 (MZ803208) was identical (701/701 bp) to that of Pythium kunmingense (GU071820.1), a rarely isolated species considered within the species complex of P. spinosum (Robideau et al. 2011). According to these results, the isolates were identified as belonging to the P. spinosum species complex, part of Pythium Clade F (Lévesque and De Cock 2004; Robideau et al. 2011). Further research is needed to clarify the exact taxonomic status of both isolates. Koch's postulates were completed using two different assays. Each assay was done twice and with carrots of the cultivar Maverick. Surface-sterilized, freshly harvested, mature carrots, in a plastic box lined with moistened sterile paper towels, were inoculated each with four CMA plugs (5-mm diameter) with actively growing mycelium of each isolate. CMA plugs, non-inoculated or colonized by a known pathogenic P. violae strain, were used as the negative and positive control, respectively. Boxes were closed to maintain humidity and incubated at 23ºC in the dark. Lesions similar to the ones caused by P. violae were observed at day 3 for all plugs of both isolates. No symptoms were observed for the negative control, even after extending the incubation to 7 days. In a more natural assay, four non-treated carrot seeds were planted in tree seedling pots (25 x 6.5 cm) containing sterilized 50/50 peat moss/sand combined with 15-ml V8 broth (Schrandt et al. 1994) with densely grown mycelium. The same inoculation treatments were used as for the carrot disk assay. Plants (one plant/pot, four plants/treatment) were maintained at 23ºC under a 16 h photoperiod with daily watering (20 ml). At 14 weeks, the carrots inoculated with P. violae and the two test isolates showed cavity spot lesions while no symptoms were observed on carrots growing in non-inoculated medium. For both assays, pathogens were re-isolated from rinsed symptomatic tissue and their identity was confirmed using the molecular analysis described above. No oomycetes were recovered from the non-inoculated carrots. Although several Pythium species have been associated with cavity spot before, this is, to our knowledge, the first report of strains within the P. spinosum species complex causing carrot cavity spot in California and elsewhere. Funding: This research was made possible by the California Fresh Carrot Advisory Board (FRA-21).

Combining disparate surveys across time to study satisfaction with life: the effects of study context, sampling method, and transport attributes

Satisfaction with life is a self-reported measure of the quality of life that has become a critical societal indicator extensively used for the evaluation and comparison of a wide range of trends and policies. This study fuses five cross-sectional travel surveys conducted from 1992 to 2018 across various geographical locations in California. Using the fused sample, we develop generalized ordered logit models to examine the effects of demographic characteristics, travel-related attributes, general and transport-related attitudinal variables, and context-control variables on individuals’ self-reported measures of life satisfaction. We find that longer commute times, mobility limitations, and a tendency to see travel as a waste of time are negatively associated with life satisfaction. To enable the use of disparate cross-sectional survey data, we incorporate context-control variables into the models. We find that life satisfaction appears to be increasing as GDP per capita increases. Among employed people, the macro-scale unemployment rate positively influences their life satisfaction. Interestingly, all else equal, we find that online opinion panel respondents have lower life satisfaction relative to respondents from other sampling methods (mainly address-based sampling), a finding that should be considered in future research using these sampling methods. Overall, this study provides a unique look at life satisfaction within a transport context, while providing an example of fusing small-scale survey datasets to study longitudinal, domain-specific, influences on variables like subjective well-being.