Spatial patterns are widespread in nature, and their formation has been studied extensively. However, the effects of spatial aggregation on the strength of species interactions are less well understood, especially in diverse ecological communities. In a field experiment with annual grassland plants in California, we manipulated the spatial arrangement-but not the number or identity-of two competitors and measured how they jointly affected a focal individual. We found that focal plants produced more seeds when their competitors were clustered than when they were mixed. These results suggest that mixed competitors generally had a more negative effect than clustered competitors. However, the effect of clustering varied across the pairs of competitor species. Competitor species that exhibited greater differences in size and/or functional traits across the spatial arrangements resulted in larger effects of clustering on focal plant seed production. Additionally, a competitive hierarchy among our study species predicted the effects of clustered versus mixed competitors on focal plant seed production. Altogether, our work suggests that the spatial arrangement of competitors changes the realized strength of competition in diverse plant communities. Given the extensive variation in spatial aggregation in plant communities, this mechanism is likely to be a powerful but underappreciated force shaping competition in nature.

Arid soil microbiomes present untapped resources of microbial diversity. Here, we describe twelve isolates, all belonging to the Methylocaldum genus. Based on metagenomic studies, the isolates represent the major clades of methanotrophic bacteria inhabiting the arid biomes of Southern California, comprising up to 0.03% of the total soil microbiota. Phenotyping of isolates indicates that they are obligate methanotrophic bacteria, some capable of methanol utilization. All strains can fix nitrogen, use nitrate and ammonia as a N-source, and have key genetic signatures of autotrophy, methylotrophy, and N2O assimilation. Based on the 16S rRNA phylogeny and whole -genome analyses, all strains are assigned to the species M. gracile. Three isolates from the rhizosphere of native Californian plants (Strains 0917, YM2 and S3V3) and GT1B-W are set apart from the other M. gracile strains, despite sharing <98% of average nucleotide identity. Microbes isolated from plant rhizosphere display 150 unique genetic features and a series of tandem gene duplications predicted to contribute to their interactions with plants, including the 20-gene polyketide biosynthesis cluster and the TRAP C4-dicarboxylate transport system. Consistent with the genetic properties that may indicate an enhancement of plant-cooperation functions, the rhizosphere isolates support the survival of plants, Boechera depauperata and Arabidopsis thaliana, under drought conditions. Based on genetic and phenotypic characteristics, we propose to designate strains 0917, YM2, S3V3, and GT1B-W as a new subspecies of Methylocaldum gracile - Methylocaldum gracile subspecies dēsertum, L.n. dēsertum - a desert, to represent the native habitat of the species. The amended description of the M.gracile species is provided.

Almonds are an essential crop for the economy of California. However, mold and mycotoxin contamination of this commodity has a serious impact on food safety and international trade. The contamination levels of molds and the aflatoxigenic potential of Aspergillus section Flavi isolates were studied on almonds collected at a processing plant in California. The mean total fungal count for 80 samples was 1.0 × 104 CFU/g, while 62 samples (77.5%) had a total mold count less than 1.0 × 104 CFU/g. The most common fungal contaminants were Aspergillus section Nigri (100% of samples), followed by Penicillium (57.5%) and Cladosporium (52.5%) species. Rhizopus, Fusarium and Alternaria spp. were less frequent. A total of 26 A. section Flavi strains were identified, with most strains (23) belonging to the L morphotype of A. flavus. In addition, two S morphotypes of A. flavus, and one A. tamarii strain were observed. Other Aspergillus species, including A. terreus and A. ochraceus were rare. High Performance Liquid Chromatography (HPLC) analysis revealed that 9 out of 13 isolated A. flavus strains produced aflatoxin B1 (AFB1) on yeast extract sucrose media. The highest levels of AFB1 were produced by two A. flavus isolates belonging to the S morphotype (78 and 260 µg/kg). Increasing temperatures and drought conditions may change the population dynamics of toxigenic mold strains on almonds, emphasizing the need to continue monitoring these fungal populations.

ABSTRACT Aim The role of species' demography and geography can be difficult to disentangle when projecting future population decline under global change. By constructing and combining species‐specific ecological models for plants in a fire‐prone Mediterranean‐type ecosystem, we explored how demography and geography can differentially affect population projections of plant species in the coming century. Location California, USA. Methods We developed a set of linked demographic‐distribution models for six Californian plant species, representing a range of life history characteristics found in the California Floristic Province. These ecological models simulate stochastic population dynamics to show how plant species might differentially respond to geographic patterns in climate change and fire regime scenarios when considering species‐specific traits. By integrating each combination of species‐specific demographic model with each of the other species' distribution models, we assessed the role of habitat loss and demographic constraints in the population declines of these plants. Results We found that all species experienced substantial population decline by 2085 under our simulations, with total species' abundances primarily influenced by habitat loss from climate and land‐use change. Species' demography had a larger influence on subpopulation‐level dynamics, especially in areas predicted to have frequent wildfires. Main Conclusions Our research underscores that responses to climate change are shaped by the interplay between species‐specific demography and geographic distribution. Though species distribution models may be able to predict changes in which areas will be suitable throughout species' theoretical niche limits, species‐specific population dynamics are critical to projecting how populations might change in abundance at more local scales. Conservation decisions should integrate both geographic and demographic factors to effectively address climate‐induced threats at both regional and local scales.

Abstract Spatial patterns are widespread in ecology, but their effects on species interactions remain unresolved, especially in diverse communities. In principle, the degree of spatial clustering could alter the concentration of higher-order interactions, which occur when one (or more) species modifies competition between two others. When species are well mixed, heterospecific neighbors have ample opportunity to modify a competitor’s interactions with other species. In contrast, species clustering can reduce the concentration of interspecific higher-order interactions. In a field experiment with annual grassland plants in California, we manipulated the spatial arrangement — but not the number or identity — of two competitors and measured how they jointly affected a focal individual. We found that focal plants produced more seeds when their competitors were clustered than when they were mixed. These results suggest that interspecific higher-order interactions generally had a stronger competitive (or weaker facilitative) effect than intraspecific ones. However, the effect of clustering varied across species. Larger differences in focal fecundity were correlated with competitors that had greater differences in size and/or functional traits between the spatial arrangements. Additionally, a competitive hierarchy among our study species predicted the effects of clustered versus mixed competitors on focal seed production. Altogether, our work suggests that the spatial arrangement of competitors changes the realized strength of competition in diverse plant communities by modifying the concentration of higher-order interactions. Given the extensive variation in spatial aggregation in plant communities, this mechanism is likely to be a powerful but underappreciated force shaping competition in nature. Significance Statement Plant species coexist in remarkably diverse assemblages throughout the world. Spatial patterns, including aggregation and intermixing, are also widespread in these communities. One potentially underappreciated mechanism that may structure the spatial dynamics of plant communities is interactions that uniquely occur in diverse systems, often called higher-order interactions. Here, we experimentally demonstrated that spatially mediated higher-order interactions operate among annual plants. These higher-order interactions, and their associated changes in competitor size and functional traits, were correlated with the competitive imbalance between competitors. Because both spatial aggregation and competitive hierarchies are widespread in nature, higher-order interactions emerging from their combination may be a more common driver of biodiversity patterns in plant communities than previously thought.

Abstract Spatial phylogenetic approaches enhance biodiversity analyses by incorporating information about species' evolutionary similarity. However, existing software packages for analysing spatial phylogenetic diversity (PD) patterns only provide full support for binary (presence–absence) community data. A major gap exists in support for occurrence probabilities (such as from species distribution models), a common and valuable data type that captures spatial uncertainty and habitat suitability gradients. While abundance data are partially supported by existing PD tools, these lack features such as geospatial data integration and flexible null model analysis. These gaps limit PD research on quantitative features of species distributions and can introduce imprecision and bias if continuous biodiversity data are thresholded to work with existing tools. Here I present phylospatial, a new R package that fully supports probability, abundance, and binary community data across a range of spatial PD analyses. The package processes all three data types in a common framework, while handling them in distinct ways at key points in the analysis pipeline. It also integrates with raster and vector data formats, providing efficient workflows for geospatial data. phylospatial provides an integrated framework for performing various types of analyses, including calculating alpha PD and endemism, testing significance using community null models, identifying beta‐PD patterns including biogeographic regions, and conducting spatial conservation prioritization. I illustrate the package's functionality with worked examples using a dataset comprising a phylogeny and modelled occurrence probabilities for 5200 species of California plants. By facilitating spatial phylogenetic analysis of quantitative data types that more accurately represent the reality of species distributions, these methods and tools help broaden the range of questions and applications that can be addressed, and can help to increase the detail and statistical rigour of these studies.

With rising antibiotic resistance, researchers are exploring sourced pharmaceuticals from local plant materials. One avenue is medicinal plants used in Indigenous communities. California State University, Channel Islands (CSUCI), is built on unceded Chumash land, affording collaboration with local Chumash communities to steward the land's natural and cultural ancestry. Utilizing plant extracts derived from California plant species, we developed an exercise incorporating the local cultural heritage and Kirby-Bauer disk diffusion assay (KB assay). In this exercise, students discuss impacting antibiotic resistance and the role of holistic discovery in creating new pharmaceuticals, examining cultural and familial teachings as a source of scientific/academic inquiry. Students then perform KB assays using plant extracts alongside antibiotics, interpret zones of inhibition for each disk, and compare them to susceptibility cutoffs provided by their lab manual and Clinical and Laboratory Standards Institute (CLSI). While most plant extracts proved ineffective against bacterial strains, students engaged in a novel method for a standardized microbiology technique. Using plants accessed from local environments expands the exercise to various regions, utilizing local flora or ingredients in their classrooms.

Direct lithium extraction (DLE) of brines after geothermal power production offers opportunities to produce environmentally benign “green” lithium; however, some environmental impact is inevitable. We examined solid waste production at geothermal power plants in southern California that are also locations for planned DLE facilities. Currently, the geothermal plants in this region produce approximately 79,800 metric tons (wet weight) per year of solid waste, which represents about 28 metric tons per GWh of net electricity production or approximately 500 mg solids per kg geothermal brine. Approximately 15% of this waste requires management as hazardous waste. Solids produced during power production represent about 0.2% of the total dissolved solids in the brine. Lithium production will require the removal of silica, iron, and other metals as part of the DLE process. Using a mass balance approach, we calculate that precipitation of silica and metals could produce up to an additional 6800 mg solids per kg brine. Calcium occurs at very high concentrations, and the amount of solids disposed in landfills will be dependent on the amount of calcium removed during lithium recovery. Our analysis shows that evaluation of brine chemistry in the context of the DLE process is useful for evaluating the potential solid waste impacts of producing lithium from brines.

Using hyperspectral and thermal imagery to monitor stress of Southern California plant species during the 2013–2015 drought

Abstract Established hedgerows of native plants on the borders of crop fields provide a variety of ecosystem service benefits in agricultural landscapes. However, their influence on weed communities is not well understood, and there are concerns that hedgerows could contribute to weed infestations on farms. To address this research gap, we examined the role of established hedgerows of native California plants on weed abundance (weed numbers and cover) and weed species richness in field borders, and in adjacent crops, in large-scale, monocropping systems compared with conventionally managed field borders (i.e., no hedgerows). Across 20 farm sites in California’s Central Valley, hedgerows on orchard crop borders reduced weed numbers by 66%, weed species richness by 59%, and weed cover by 74%. On annual field crop borders, hedgerows reduced weed numbers by 71%, weed species richness by 60%, and weed cover by 70%. In orchards, hedgerows also reduced weed intrusion into the adjacent crop interior, with significantly lower weed cover to the first tree row (area directly underneath the trees), weed species richness to the 10-m tree row, and weed numbers to the 10-m avenue (area between the tree rows). Yearly management practices and associated costs for weed control in established hedgerows were significantly less than for conventionally managed field borders. This study highlights the effectiveness of native hedgerows as a sustainable nature-based solution for reducing weed pressure and management inputs on farms.

Animal damage from opossums to carnivorous plants in suburban Southern California.

Providing forecasts of pressure fluctuations and changes will aid in selecting appropriate maintenance strategies to optimize efficiency and costs. This paper presents a deep-learning-based model to forecast the degradation evolution of membrane biological fouling in RO (Reverse Osmosis) systems. Although applying deep learning in forecasting still faces many challenges, applying convolutional operations in convolution 1D has yielded promising results for sequential data, particularly time series data. Thus, in this paper we study and develop the 1D convolution operation-based Temporal Convolutional Network (TCN) model to predict pressure dynamics at both ends of the RO vessel. In addition, since the deep learning technique has yet to be widely explored in this field, thus we also need to pre-process the data collected from the Carlsbad Desalination Plant in California, such as the proposed model can identify complex relationships between timestamps and pressure features. The experiment results were evaluated and compared with other existing models, such as LSTM, CNN &amp; LSTM, and GRU. The obtain results show that the TCN-based prediction model had the slightest error in the test dataset.

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals.

Tomato (Lycopersicon esculentum) is a globally consumed vegetable with a significant role in the food industry. In 2021, global tomato production exceeded 189.1 million metric tons, emphasizing its economic importance. This research delves into the challenges posed by wastewater generated during tomato processing and explores sustainable strategies for managing it. Case studies were conducted in tomato processing plants in California, Kadawa (Kura LGA, Kano State), and Kumo (Gombe State) to assess their wastewater management practices. The findings revealed a lack of uniform wastewater collection systems in the studied factories, highlighting the need for improved environmental sustainability. As Bauchi State's population and industrialization grow, addressing the wastewater issue becomes increasingly urgent. Inadequate wastewater management can have detrimental effects on human health, water supplies, and the environment. To mitigate these challenges, this study proposes the integration of sustainable techniques in tomato processing factories. The objectives include quantifying wastewater generation, designing a functional factory layout applicable to other food processing industries, and creating a factory design that promotes sustainable water use. Site selection criteria were established, focusing on accessibility, labor availability, utilities, expansion potential, topography, and environmental impact. After a thorough evaluation, a site in Hardawa, Misau Local Government Area of Bauchi State, was identified as the most suitable location for the proposed tomato processing factory. The factory's design brief includes provisions for production facilities, administrative blocks, and worker facilities. Space requirements were determined based on the anticipated activities within each section. The proposed design emphasizes water harvesting and recycling to reduce wastewater generation and promote sustainability. In conclusion, this research underscores the importance of addressing wastewater management in tomato processing factories. By implementing sustainable design principles, factories can minimize environmental impact, enhance operational efficiency, and demonstrate commitment to ethical business practices. The proposed design offers a comprehensive solution to the wastewater challenge, ensuring compliance with environmental standards and safeguarding natural resources. Recommendations include collaboration between factory management and government authorities, the adoption of sustainable design approaches in future tomato processing facilities, and the allocation of resources for ongoing maintenance. Embracing these recommendations will contribute to more responsible and environmentally conscious tomato processing practices.

The secondary metabolites of traditional medicinal plants are a potential source of new antibiotics (6).Two local plants with a tradition of use by California native tribes are Grindelia stricta platyphylla (coastal gum weed), a remedy used internally for respiratory and skin ailments and Iris douglasiana (Douglas Iris), also used to treat skin sores.Aqueous, ethanolic, acetonic, and ethyl acetate plant extracts were tested for antimicrobial activity, using the disk diffusion method, against gram -positive and gram -negative bacteria to determine the most effective extraction solvent.The ethyl -acetate extracts of both plants (Grindelia and Iris) showed the most antimicrobial activity, inhibiting growth of gram -positive Mycobacterium phlei and Staphylococcus aureus.For Grindelia aerial parts: the MIC was 0.0898 g/l against M. phlei and 0.346 g/l against S. aureus, the MBC was 0.1796 g/l against M. phlei but did not kill S. aureus.For Iris rhizomes: the MIC was 0.1944 g/l against M. phlei and 0.0970 g/l against S. aureus, the MBC was 0.1944 g/l against S. aureus but did not kill M. phlei.Preliminary phytochemical screening was also done on the extracts.Our hypothesis is that the California Native plants, Grindelia stricta platyphylla and Iris douglasiana, will have antimicrobial activity.

Viruses transmitted by the whitefly (Bemisia tabaci) are an increasing threat to cucurbit production in the southwestern United States as well as many other cucurbit production regions throughout the world. The crinivirus, cucurbit yellow stunting disorder virus (CYSDV), has severely impacted melon production in California and Arizona since its introduction to the region in 2006. Within the past few years, another crinivirus, cucurbit chlorotic yellows virus (CCYV), and the whitefly-transmitted ipomovirus, squash vein yellowing virus (SqVYV) were found infecting melon plants in California's Imperial Valley as well. Both CYSDV and CCYV, as well as an aphid transmitted polerovirus, cucurbit aphid-borne yellows virus (CABYV), occur together in the region and produce identical yellowing symptoms on cucurbit plants. Mixed infections of these four viruses both in the Sonoran Desert and other regions pose challenges for disease management and efforts to develop resistant varieties. A multiplex single-step RT-PCR method was developed that differentiates these viruses from one another, and this was used to determine the prevalence and distribution these viruses in melon samples collected from fields in the Sonoran Desert melon production region of California and Arizona, USA during spring and fall melon seasons from 2019 through 2021. TaqMan probes were also developed, optimized, and applied in a single-step multiplex RT-qPCR to quantify titers of these four viruses in plant samples, which frequently carry mixed infections. Results of the multiplex RT-PCR analysis demonstrated that CYSDV is the predominant virus during the fall season, whereas CCYV was by far the most prevalent virus during spring seasons each year. Multiplex RT-qPCR was used to evaluate differential accumulation and spatio-temporal distribution of viruses within plants and suggested differences in competitive accumulation of CCYV and CYSDV within melon. This study also provides the first official report of SqVYV in Arizona, USA, and offers an efficient method for virus detection and quantification for breeding and disease management in areas impacted by cucurbit yellowing viruses.

Sporodochia are dense masses of fungal hyphae bearing asexual conidia. For Fusarium oxysporum, sporodochia are known to produce airborne conidia and enhance the dissemination of this otherwise soilborne pathogen. Sporodochia are small and transient, and they are documented for only a few formae speciales of F. oxysporum. This study reports airborne conidia and sporodochia produced by F. oxysporum f. sp. fragariae, the cause of Fusarium wilt of strawberry, in the Monterey Bay region of California. Sporodochia were discovered in 21 of 24 Fusarium wilt-diseased fields surveyed for this study and were readily observed on most symptomatic plants in these fields. Only necrotic tissues bore sporodochia, and they were most frequently observed on petioles and peduncles. Sporodochia covered significantly greater lengths of peduncles than petioles, extending from the base of the plant toward the upper part of the canopy. A stolon hosted the longest stretch of sporodochial growth, found covering the stolon's entire 35-cm length and the base of the daughter plant. Macroconidia were produced by all sporodochia samples, and we did not find microconidia on any samples. An initial series of experiments confirmed the potential for conidia produced by sporodochia to disperse with wind over short distances. The prevalence of sporodochia producing airborne spores of F. oxysporum f. sp. fragariae has great importance for disease management and biosecurity. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Geitonogamy, the transfer of pollen from one flower to another on the same plant, is often the primary means of self-pollination in flowering plants. For self-compatible plants, self-fertilization may lead to greatly reduced offspring fitness via inbreeding depression. For self-incompatible plants, geitonogamous pollen transfer can result in low seed set, even when stigmatic pollen loads are substantial. For multiple self-compatible, native California plants, we found that honey bees visited more flowers per plant than native insects, and that offspring resulting from pollination by honey bees had reduced fitness relative to those resulting from native insect pollination. Here we investigate whether honey bees generally make more geitonogamous visits than other pollinators using data from a global survey of 41 manuscripts that reported floral visitation data. Compared to the average of all non-honey bee visitors in a plants pollinator assemblage, honey bees visit significantly more flowers per plant, though they do not differ from the non-honey bee visitor with the highest rate of geitonogamous visitation. However, the disparity between rates of geitonogamous visitation by honey bees and non-honey bee visitors is a function of the frequency of honey bees relative to non-honey bee visitors. As honey bees become increasingly numerically dominant, there is a trend for their rates of geitonogamous visitation to increase, accompanied by a significant decline in flowers visited per plant by non-honey bee visitors. While we found that honey bees visited more flowers per plant compared to the average of other visitors, large or eusocial pollinators were as likely as honey bees to be the most geitonogamous visitor.

We measured concentrations of SARS-CoV-2, influenza A and B virus, respiratory syncytial virus (RSV), mpox virus, human metapneumovirus, norovirus GII, and pepper mild mottle virus nucleic acids in wastewater solids at twelve wastewater treatment plants in Central California, USA. Measurements were made daily for up to two years, depending on the wastewater treatment plant. Measurements were made using digital droplet (reverse-transcription–) polymerase chain reaction (RT-PCR) following best practices for making environmental molecular biology measurements. These data can be used to better understand disease occurrence in communities contributing to the wastewater.