Month: March 2025

The total cost of the all the UAEs, depicted by the Batch Generic Box in SuperPro, was the most expensive , followed by the belt press filters . For better cost estimates of the UAEs used in the model, a quote was obtained from an industrial manufacturer named REUS® . Pricing forthis equipment was reduced by about 5%, assuming a conversative approach on the expected discount for bulk purchase of equipment .The OPEX, otherwise referred to as annual operating cost, and COGS are shown with and without depreciation, insurance, and local taxes. The annual operating cost and COGS without including depreciation are more representative of the expected cost for production since depreciation is typically spread over years to effort to expense cost over time while simultaneously lowering the value of the asset . Also depreciation is not a cash outlay so does not have a negative impact on profitability . Here, depreciation was calculated using the straight-line method with a depreciation period of 10 years and salvage value of 5% of the DFC. Insurance was estimated to be 1% of the DFC and local taxes were estimated to be 2% of the DFC. As expected, evaluation of the facility’s annual operating costs and COGS including depreciation, insurance and local taxes are higher compared to without.An analysis was performed to investigate the effect of factors such as depreciation, insurance, factory expenses and local taxes on the economics of the model. A breakdown of the annual operating for each case are shown in Figure 3.4 through 3.6. In all cases, nursery grow bag the annual operating costs are composed of the following: raw materials, labor dependence, facility dependence, Laboratory , Quality Assurance , Quality Control , consumables, waste treatment and utilities.

When the first of the three cases were analyzed, it was determined that the largest contributor to the annual operating costs was the facility dependent cost. Here, facility dependent costs include maintenance, depreciation, insurance, taxes, and factory expenses. Maintenance of each equipment was determined using equipment-specific multipliers, default values provided by SuperPro. No pre-existing depreciation of equipment was assumed in the model. Local taxes were assumed to be 2% of the DFC, in alignment with values from municipal tax charts for South Dakota . Percentages for insurance and factory expenses were estimated using a previous SuperPro model performed internally which collaborated with a plant based bio-manufacturing facility for pricing. The second largest contributor to the annual operating cost was the raw materials. A breakdown of the raw materials and their costs can be seen in Figure 3.7. Here, raw material costs amounted to a value of $5.7 million per year. A list of the raw materials used, the quantity used annually, and their total cost can be seen in Table 3.7. Ethanol, or ethyl alcohol as defined in SuperPro, had the biggest yearly expense out of all the raw materials, totaling over $4.1 million. The model estimates 6.1 million kg of ethanol needed to produce 100 MT of resveratrol. It was determined that the cost of ethanol is over two thirds of the total raw material cost, accounting for 71% of the raw material cost. The price used in the model was $2.00 per gallon of ethanol. This value was retrieved using data by Market Insider which tracks the price of commodities like ethanol daily . For the second case, which only excludes depreciation as a factor contributing to facility dependent costs, the largest contributor was raw materials. Raw materials costs are the same in all three cases but now account for 50.9% of the annual operating cost, with the cost of ethanol remaining the largest contributor.

The second largest raw material cost is the knotweed rhizomes used in the process. Roughly 7.3 million kg of knotweed rhizomes are needed in the process simulation to produce 100 MT of resveratrol. The cost for producing a kg of knotweed rhizomes was estimated to be about $0.19, totaling $1.4 million or 24.3% of the raw materials cost. Estimates for a kg of knotweed were calculated to include costs associated with pre-planting, harvesting, postharvesting, farm equipment operating costs, and cash overhead . A breakdown of these calculations is shown in Chapter 2. Following the second case, which provides a clearer estimate of annual operating cost because depreciation is not included, labor was the third highest contributor to the annual operating cost.Here, labor dependent costs refer to the costs associated with labor hours required to effectively operate the downstream processing section. Labor cost for the upstream production of Japanese knotweed rhizome was already included in the overall unit cost for a kg of knotweed. In this model, the downstream operators earn a wage of $22 per hour. The downstream labor cost includes 40% benefits factor, 10% operating supplies factor, 20% supervision factor, and 60% administration factor. An example of this distribution is as follows: for every $20 paid to an operator for an hour of work, there is an additional cost of $8 for benefits, $2 for supplies, $4 for supervision and $12 for administration. Total labor hours amount to 20,398 hours per year with operators devoting most of their time to the blending tank used as an adsorption vessel. This was in alignment with our estimates since the blending tank is one of the two equipment with the highest number of operations needed compared to every other unit in the model. The next factor contributing to the annual operating cost was the Lab, QA, and QC group, which accounted for less than 1 percent. No funds were allocated to on-going research and development whereas, QA and QC were estimated to be 5% of the total labor cost.

Only one consumable was defined within the process, as listed in Table 3.8. This consumable was the macroporous resin, NKA-11, which is used within the batch adsorption vessel . The annual cost of the resin amounted to $130,536. Pricing for this resin was estimated using commercial values found online by a large scale supplier Co., Ltd.. Waste treatment costs were also incorporated into the annual operating cost calculations. This waste treatment category incorporates the price to safely dispose of different waste streams generated in the facility. Exiting waste streams are classified under one of four groups: organic waste, aqueous waste, solid waste, or gaseous emissions. Here, the cost of emissions was negligible due to low concentration of nitrogen, oxygen, ethanol, and water vapor being released into the atmosphere. The cost to dispose of each group is as follows: organic waste is $0.01/kg, aqueous waste is $0.001/kg and solid waste is $0.01/kg. The annual amount of waste produced by the process is about 44 million kg, estimated at about $234,000 per year in waste treatment costs. While the cost for waste treatment of each group remained similar, the largest contributor to the cost is attributed to organic waste, which include the disposal of the biomass. Resin disposal costs were negligible. These results are in alignment with the cost and use of raw materials throughout the process. A breakdown of the annual waste treatment contributors is shown in Figure 3.8.Utilities are the last factor which contribute to the annual operating cost. A detailed breakdown by utility type used in the process model annually can be seen in Figure 3.9. Three utility types were used in the model: standard power, steam, and chilled water. Pricing for each utility were set as, $0.10 per kW-h of power, $12.00 per MT of steam, and $0.40 per MT of chilled water. The cost of std power was the highest of all three utilities. Std power was used to operate all major equipment including but not limited to the stirred reactor acting as the enzymatichydrolysis unit, plastic growing bag the blending vessel serving as the adsorption vessel, the ultrasonic assisted extractor unit, and the grinder. Surprisingly, the grinder responsible for crushing and homogenizing the plant material into powder required the largest amount of power to operate. Total utilities costs only amounted to a 1% of the annual operating cost. In this model, the annual operating cost for each case described above were used and divided by 100 MT, resulting in a COGS of $150/kg, $111/kg, and $79.4/kg. A summary of both the process parameters and economic analyses from the base case model is shown in Table 3.9.Throughout the design of the simulation model, certain parameters were initialized to match resveratrol production practices described in both patents and scientific literature. When extrapolating bioprocessing parameters from these sources, a conservative approach was taken so that the lower values from a range of data was used in the model in effort to portray realistic results expected during large scale production. An example of this is including a loss of 5-10% material during a filtration step, instead of expecting an 100% recovery every batch. As acknowledged in Chapter 3, certain bioprocessing parameters used in the model were assumptions, thus leading to some uncertainty whether the process outputs were reliable. For this reason, a sensitivity analysis was performed to assess how a certain variation effects the economics of the model, specifically the CAPEX, OPEX, and COGS. Similarly, it is acknowledged that there are multiple designs which can be implemented to produce 100 MT of resveratrol and this model simply serves as an example of one method. Therefore, certain scenario analysis were performed to assess the effect of design and production amount on key economic parameters.To assess the sensitivity of the base case model, we varied certain process parameters to investigate their impact on the CAPEX, OPEX, and ultimately the COGS. One parameter which was defined in the SuperPro model using a conservative approach was the concentration of resveratrol present in the Japanese knotweed rhizome used for processing . Figure 4.1 demonstrates the relationship between COGS and when the concentration of resveratrol per knotweed rhizome is increased up to 3 mg/g FW. The concentration of polydatinwas also increased proportionally along with resveratrol. Each facility simulation is redesigned for each concentration tested while still reaching 100 MT resveratrol annually.As expected, when a larger concentration of resveratrol was modeled to be present within the Japanese knotweed rhizome entering the process, the model resulted in a decrease in both the CAPEX and COGS. The largest drop in CAPEX occurs directly when the concentration of resveratrol is increased an order of magnitude to 1.0 mg/g. The decrease in CAPEX from 0.5 mg/g to 1.0 mg/g is $13 million, over 3-fold larger than the average drop between increments. Expectedly, the COGS also decreases the largest amount between the first two concentrations. The COGS drops a value of $48/kg to a value of $102/kg, a 32% drop. Values for both economic parameters begin to plateau around a concentration of at 1.5 mg resveratrol/g FW, approximately at values of $27.8 million and $88/kg for CAPEX and COGS , respectively. It was discovered that resveratrol is found in a wide range of concentrations in Japanese knotweed rhizomes. A table listing different resveratrol concentrations found in Japanese knotweed is shown in Chapter 2. As mentioned, an average value of the total resveratrol concentration in knotweed, including polydatin, was about 7 folds higher than just free resveratrol. Using information on resveratrol concentrations for Japanese knotweed rhizomes grown specifically in North America1 , we calculated an average concentration value of 2.6 mg resveratrol/g FW. If a future process used rhizomes under similar conditions , our simulation suggests a cost decrease of about one third for CAPEX and 43% for COGS compared to our base case model operating at 0.5 mg Rsv/g. Notably, the same authors that describe an average concentration of 2.6 mg Rsv/g Japanese knotweed roots also mention certain samples contain concentrations as high as 12 mg Rsv/g FW and 12 mg Polydatin/g FW. Using this information, a simulation operating at the same conditions as the base cased was modeled using concentrations of resveratrol and polydatin at a 1:1 ratio at 12mg/g for each stilbene compounds. The same economic analysis was performed on the 12mg/g case, the resulting CAPEX, OPEX, and COGS values are shown in Table 4.1.In the previous chapter, the cost of ethanol was identified to be the major contributor to the annual operating cost and the largest bottleneck.

The marginal ancestral state likelihood estimates of each host for all internal nodes of the ML phylogenetic trees were calculated using the re-rooting method of Yang et al. in the R package phytools , and mapped using the package APE . This method uses the phylogeny of extant taxa to reconstruct ancestral traits of extinct ancestors by analyzing phylogenetic parameters , along with a model of nucleotide substitution, to build posterior probabilities of character states at each interior node by randomly re-rooting the tree at each internal node and calculating the probability of observing the extant distribution of traits over all possibilities of that internal node character identity. The ML estimates at each internal node were calculated based on both the equal rates transition model and the symmetrical rates transition model . The fit of the two models to the data was compared using the Akaike information criterion and can be seen in supplemental table . Scoary was used to test if the pan-genome was correlated with hosts at either the super-order scales or the genus scale by conducting a Fisher’s Exact Test . FET measures the association of each gene in the pangenome to a trait of interest, plastic planters which in this case is plant host. While FET requires no association between datapoints, Scoary uses a phylogeny in order to remove lineage specific interdependencies and corrects the p-value based on those interdependencies.

Significance was evaluated by the “worst pairwise comparison P”, for the phylogenetic corrections, not the naïvep-values from FET. Individual analyses were conducted to test for correlation of gene presence and absence with each of the 29 coded host groups .California is the center of American fruit, vegetable and nut production and is a globally important exporter of plants . As a hub of international plant trade, California has been both a source and a sink for the introduction of novel phytopathogens across the globe. Xylella fastidiosa ssp. fastidiosa is a bacterial pathogen that impacts grapevines and almonds in California. X. fastidiosa subspecies fastidiosa, was likely of Central American origin and possibly introduced via coffee imports to the United States in the 1800s . After its introduction into the U.S., the pathogen has continued to spread, including recent introductions into Spain and Taiwan . In California, outbreaks of X. fastidiosa ssp. fastidiosa have been devastating to the grape industry. In contrast, infections have not caused notable issues in the agriculturally important regions of Central America, where the ssp. has likely existed in the ecosystem for at least thousands of years . This is not a universal trait of X. fastidiosa in C. arabica as opposed to Vitis. In Brazil, X. fastidiosa ssp. pauca infections reduce C. arabica yields, despite the pathogen’s long history in the region . X. fastidiosa is complex in its strain specificity to various host plants, with many conflicting examples of host susceptibility in geographic areas. Nonetheless, documenting host susceptibility to different subspecies and strains of the pathogen is vital. Clarifying the process of host jumps in X. fastidiosa is urgent for global agricultural security, as new outbreaks continue to put plant health at risk. While it is likely that X. fastidiosa ssp. fastidiosa is broadly present in Central America, currently only Costa Rica has reported this pathogen.

Here we refer to “Costa Rican” strains as our representative Central American strains, although there is certainly much larger diversity of X. fastidiosa in the region. X. fastidiosa ssp. fastidiosa frequently infects C. arabica in Costa Rica without substantially impacting production, although subtle leaf curling symptoms correlate with X. fastidiosa infections . In Costa Rica, X. fastidiosa ssp. fastidiosa also infects periwinkle , guava , and avocado . While symptoms have been reported in these hosts, disease severity and progression have not been thoroughly evaluated. Some strains of X. fastidiosa ssp. fastidiosa have been detected in Vitis in Costa Rica, where V. vinifera is not commonly grown but only sequence-type data was collected for these strains. However, a recent study found that related strains from Costa Rica did not cause infections in Vitis . Given these results, infectivity and virulence by the same strain may differ between Vitis and C. arabica. A clear phylogenetic delineation of pathogenicity, which requires extensive crossinoculation experiments, has not yet been determined. This study begins to test the adaptation of the U.S.-introduced clade to Vitis cross-inoculation and computational methods. Within the genus Vitis, there is wide genetic diversity conferring variation in both tolerance and resistance to Pierce’s disease , the disease caused by X. fastidiosa ssp. fastidiosa infections in grapevines. Differences are seen between species such as the naturally tolerant V. arizonica and Muscadinia rotundifolia, and the susceptible V. vinifera . This variation has recently been used to hypothesize that there might have been PD present in the U.S. for longer than has been estimated by all evolutionary data . But disease susceptibility also varies largely within V. vinifera , an intensely bred species that includes a wide and diverse range of cultivars. In contrast to crespera in Costa Rica, PD in the United States is quite virulent, persistent, and economically damaging. Infection with the pathogen causes severe symptoms such as stunting, leaf scorch, shriveled fruit, and eventual plant death in many cases . There are several additional adversely impacted hosts of species within the introduced clade. Of economic importance is almond suffering Almond Leaf Scorch . Other hosts of strains in the introduced clade include maple , plum , ragweed , sweet cherry , and western redbud . C. arabica is a unique host for X. fastidiosa. It has widespread documented infectivity coupled with non-deadly symptoms, and it was likely the source of two economically devastating introduction events: to the United States in grape and to Italy in olive . Nonetheless, C. arabica is generally understudied as a host plant. Data from Brazil show that C. arabica and Citrus strains of X. fastidiosa ssp. pauca do not cross-infect , showing variation in host specificity. However, C. arabica is also a host of at least two of the three major subspecies of X. fastidiosa, showing broad susceptibility. It has been hypothesized that strains of X. fastidiosa do not need to undergo many genetic changes in order to infect C. arabica, potentially making it a host susceptible to low-cost host jumps . While ssp. pauca and ssp. fastidiosa have both been documented to infect C. arabica, slow progression and low virulence have led C. arabica to be described as a “latent carrier” . So far, research has shown that the strains able to infect C. arabica are quite diverse . The infectivity of X. fastidiosa ssp. multiplex has never been tested in C. arabica. Subspecies multiplex is native to the south-eastern United States and is present throughout the U.S., South America, and Europe . However, the advent of coffee production in California coupled with historic documentation of ssp. multiplex in Argentina, Brazil, and Paraguay, call for investigation of whether C. arabica can serve as a host to ssp. multiplex . Upon introduction to the U.S., X. fastidiosa was exposed to new hosts, climate, vectors, and agricultural practices alongside a rapidly growing agricultural industry. While it is likely that the introduced strains were exposed to selective pressures that increased specificity to the conditions in the U.S., mainly climate and host, plastic plant nursery pot that is not the only possibility. It is also plausible that instead of adapting to the specific pressures in the new environment, the introduced population of strains became generally hypervirulent.

This could explain the high level of virulence towards V. vinifera in the U.S. compared to the lower virulence observed in Costa Rica. We investigated both neutral and adaptive changes during the process of naturalization for this pathogen using whole genome sequences.In March 2022, 120 coffee plants were divided into equal groups and inoculated with either a buffer control or one of four CA strains of X. fastidiosa, ssp. fastidiosa: Napa1 , ALS17T5 , Je115 or subspecies multiplex ALS15T2 , or the sterile succinate-citrate-phosphate buffer control . While Napa1 and Je115 are both isolated from V. vinifera, they are from different climates and clades of X. fastidiosa, and might have experienced distinctive selective regimes . ALS17T5 was isolated from a symptomatic almond plant and is in one of several clades of strains infecting only almond trees, however this clade is nested within other clades that infect Vitis . Disease-free C. arabica plants were donated from Frinj coffee company for this experiment. As positive controls, 20 V. vinifera cv. Chardonnay were inoculated as well as 50 Helianthus annus. V. vinifera is known to be highly susceptible to subspecies fastidiosa but not subspecies multiplex, while Helianthus annus is susceptible to both subspecies . Cell suspensions were prepared by suspending week-old cells grown on solid medium in SCP buffer just prior to inoculation. Each suspension was made by scraping 10 streaks of 20µL into 1 mL of buffer. Inoculations were conducted using 2 10µL beads of inoculum and a 00-size entomological pin used to pin prick through the bead of cell suspension several times until the inoculum absorbed into the plant xylem. Inoculations were conducted on small plants with typically 3 full-leaf pairs, and two inoculum beads were placed just above and below the center leaf pair. Plants were not waThered the morning of inoculation to optimize absorption of the inoculum into the xylem vessels. Symptom measurements took place in August, September, October, December, 2022, and January and April 2023, following the March 2022 inoculation. In August and September 2022, all internode lengths along the main stem of the plant were measured along with the heights of each plant. In October, December, January, and April, only the total plant heights were measured to detect stunting. Plants were also visually assessed for foliar scorching.Control and experimental plants were tested for the presence of X. fastidiosa via qPCR or culturing. DNA was extracted using a DNeasy plant mini kit and then quantified using qPCR with a primer pair targeting the gene encoding RecF, RecF1_F+R; for qPCR protocol see Sicard et al. . qPCR was run in duplicate with positive and negative controls on each plate; all samples with Ct values of 37 or higher were considered “undetected” and were considered negative. Culturing was conducted using approximately 0.1 grams of petiole tissue, or the entire petiole and midrib of a C. arabica leaf. Samples were surface sterilized, chopped, ground in a Polytron, and plated on PWG media using the method from Hill and Purcell . On April 3 rd , 2022, petioles that were directly above the inoculations site were sampled from all 50 sunflower plants and X. fastidiosa populations were measured via both qPCR and cell culturing. On June 16th , 2022, V. vinifera plants were sampled from and also visually assessed for symptoms. On July 14th , one leaf from the 2 nd leaf pair above the inoculation point was used for detection via qPCR. On August 1 st, ,15th , and 18th , one leaf from the 2 nd leaf pair above the inoculation point was cultured from each C. arabica plant. On October 4 th , 2022, the opposite leaf was taken for qPCR. While Vitis is not as susceptible infections from ssp. multiplex, typically there is some detectable infection, however those infections have 10-100 fold lower population sizes than infections by ssp. fastidiosa . In February – April 2023, we cultured from the leaves using a different method due to loss of leaves from the ~ 8 cm above the inoculation point. The lowest 8 leaves were collected, and the petioles were pooled for sterilization, tissue grinding, and plating.All analyses were performed using R statistical software version 4.2.3 . Linear mixed models were built using the package lme4 to add a random effect to account for repeated sampling of the plants over the course of the experiment . As analyses of virulence, we tested the effects of variety and the interaction between treatment and sampling date on both internode length, and separately on plant height , each with a random effect of plant ID using the lmer function of lme4. As an analysis of infection persistence via detection assays, we tested the effects of variety and the interaction between treatment and sampling date on the count of plants that tested positive using a generalized linear mixed model with binomial error using the package glmmTMB and the function glmmTMB .

When S. ×utahensis plants dehydrated as a result of decreasing substrate volumetric water contents, plants closed their stomata to reduce transpiration and stomatal conductance as a drought acclimation to maintain plant water status and prevent water losses and further dehydration . Although CO2 uptake is limited when stomata are closed and stomatal conductance reduced , plants had a lower proportion of visibly wilted leaves in this study or better aesthetic quality under drought conditions in other reports. These plants were considered drought tolerant in ornamental plant evaluations in semiarid regions in Australia and the United States . Shepherdia ×utahensis reduced its midday stomatal conductance at lower water availability and can be considered as a low water-use landscape plant. Plants have the capacity of regulating stomatal conductance that is related to their habitat aridity. Kjelgren et al. reported that plants native to arid regions, such as Dianella revoluta ‘Breeze’ and Ptilotus nobilis , showed greater reduction of stomatal conductance compared with those from humid areas. Because of restricted transpiration, plants with acclimation capability may reduce leaf size to enhance convective heat loss to mitigate heat stress that causes high leaf-to-air VPD and leaf wilting . The fact that leaf-to-air VPD increased when substrate volumetric water content decreased is likely a direct consequence of increased leaf temperature because leaf vapor pressure is estimated by leaf temperature.

To avoid heat stress, french flower bucket leaf energyis balanced primarily using sensible heat loss under drought . The efficacy of sensible heat loss relates to boundary layer resistance, which is positively correlated to leaf width . Under drought conditions, cell division and leaf expansion are limited , and smaller leaves are beneficial for dissipating heat through convection and conduction to maintain leaf temperature close to air temperature . In this study, S. ×utahensis produced smaller leaves under water stress and leaf size of plants grown at the substrate volumetric water content of 0.05m3 ·m−3 was 51% smaller than those at 0.40m3 ·m−3 . This result is in line with previous studies that consistently reported reductions in leaf size under water stress for drought-tolerant ornamental plants . For instance, Zollinger et al. suggested that small leaves allow Lavandula angustifolia and Penstemon ×mexicali ‘Red Rocks’ to reduce water loss when irrigation intervals were increased from 1week to 4weeks. Toscano et al. also found that leaf size of Viburnum tinus ‘Lucidum’ decreased by 19% to acclimate to drought stress. Shepherdia ×utahensis decreased total leaf area under water stress as a result of reductions in leaf number and size . However, plants with decreased total leaf area have fewer stomata and less light interception, which controls transpiration and leaf temperature, respectively . Reduced total leaf area has been reported as a means of avoiding drought stress in ornamental plants such as Lavandula angustifolia, Pittosporum tobira , and Viburnum tinus ‘Lucidum’ . The root growth of S. ×utahensis was enhanced at low substrate volumetric water content, while shoot growth was inhibited, resulting in a higher root-to-shoot ratio , which helps plants to obtain water more efficiently. Rosa hybrida ‘Ferdy’ and Populus cathayana have been observed to increase root growth to maintain water status under water stress .

Drought-tolerant plants native to the western United States also produce small leaves and deep roots to reduce water demand and loss and increase water uptake . In this study, as substrate volumetric water content decreased, leaves of S. ×utahensis curled as stem water potential became more negative. At the substrate volumetric water content of 0.05m3 ·m−3 , the leaf curling index was 0.17, suggesting that the light interception area was 83% that of flattened leaves. Similarly, Dianella revoluta ‘Breeze’ and Ctenanthe setosa have been shown to minimize sunlight exposure through leaf curling under water deficit . Although light-harvesting efficiency is reduced, leaf curling limits water loss from transpiration and protects plants from overheating to sustain photosystem functions and other biochemical/physiological processes . In addition, as the rooting substrate became drier in this study, specific leaf area decreased, indicating that leaves became thicker , which prevented leaves from overheating. Plants may decrease specific leaf area to acclimate to water stress as reported in Ptilotus nobilis . The trichome density of S. ×utahensis in this study was affected by substrate water availability and plant water status . Water-stressed S. ×utahensis produced densely packed trichomes, resulting in a silvery appearance, while well-watered plants had fewer trichomes to cover epidermal cells and exhibited a greener color . Trichomes promote leaf reflectance , which helps balance energy and reduce heat stress . Positive effects of trichomes on leaf reflectance of visible light have been reported on Verbascum thapsus and Salix commutata . However, because trichomes are broad-spectrum reflectors , the reflectance of PAR, blue, green, and red light is proportional to the trichome density . When substrate volumetric water content decreased, the reflectance of green light did not increase as much as blue light and red light due to the chlorophyll in the epidermal cells . Increased leaf reflectance has been shown to sacrifice the efficacy of light-harvesting pigments and reduce the net assimilation rate when plants are grown in drier conditions. Previous research also suggested that trichomes improved the reflectance of near infrared light . However, in this study, denser trichomes produced in drier substrate did not affect near infrared light reflectance of S. ×utahensis . Slaton et al. reported similar results that near-infrared light reflectance was not affected by increased trichome density in 48 species. More studies are needed to evaluate the effects of trichomes on near-infrared reflectance. Increased trichome density has smaller effects on decreasing gas exchange coed with the effects on leaf reflectance . However, densely packed trichomes covering the stomata of S. ×utahensis may increase resistance to transpiration and reduce water loss . Leaf trichomes also increase leaf roughness and increase the laminar boundary layer to restrict air movement across leaf surfaces to reduce transpiration . Eriogonum corymbosum and S. rotundifolia produce leaf trichomes for better protection from wind and to maintain water status . Densely packed trichomes add an atmospheric boundary layer that imposes additional resistance to water vapor diffusion . However, CO2 influx is also limited by the boundary layer resistance, decreasing the net assimilation rate . Although trichome-induced boundary layer resistance has a smaller effect on transpiration than stomatal conductance , it still provides an advantage for desert plants to survive in dry and hot conditions. The genetic regulation of trichome density of Caragana korshinskii has been reported by Ning et al. . However, it is unclear how xeric plants change their trichome density to acclimate to drought conditions. A negative correlation between leaf trichome density and leaf size or epidermal cell size occurred in this study , which suggests that cell expansion maycontrol trichome density. Low trichome coverage fraction, which was related to greater space between trichomes, showed when epidermal cell density decreased, indicating cell expansion may coordinate trichome density. Ascensão and Pais reported the number of trichomes is determined during leaf lifespan, and leaf cell differentiation does not affect trichome number. Similar results showed in our research that plants had similar total numbers of trichomes per leaf at different substrate volumetric water contents. This may indicate that S. ×utahensis develops trichomes independent of leaf development. In fact, trichomes develop at the early stage of leaf development and often earlier than stomatal development . For instance, trichomes of Inula viscosa are fully developed and reach mature size when leaves are 2mm long; however, a mature leaf is 6–8cm long .

Ocimum basilicum forms trichomes at an early stage of leaf development and trichomes then grow independently . In the same study, trichomes covered young leaves but became more widely spaced when leaf cells started to expand . In our study, the total number of epidermal cells per leaf was similar on plants at different substrate volumetric water contents, bucket flower which indicates cell differentiation might have minor effects on regulating trichome density. In contrast, cell expansion might be the main factor for regulating trichome density because leaf size, epidermal cell size, and the space among trichomes changed along with substrate volumetric water contents and correlated significantly with trichome density of S. ×utahensis . Ehleringer found a negative correlation between leaf size and trichome density of Encelia farinosa, but cell size was not determined. Cell enlargement at high soil moisture levels amplified leaf size and the space among trichomes, reducing the trichome density on the S. ×utahensis leaves in this study. The relationships between trichome density, epidermal cell size and density, and leaf reflectance might indicate changes in cell size predominantly controls trichome density to modify leaf reflectance. Modifying leaf reflectance via the change in cell size helps rapidly acclimate to environmental change without compromising whole leaf function . Cell-expansion driven leaf anatomic change has been widely reported on adjusting stomatal density . For instance, Murphy et al. observed that cell expansion was the predominant factor for coordinating vein and stomata density of eight angiosperm species under sun and shade. Stomatal density decreases and the size of guard cells increases when leaf water potential increases , suggesting cell expansion not only enlarges the distance between epidermal appendages but also increases their size. Environmental factors also promote leaf trichome density. Such factors include increased leaf-to-air VPD and drought , all of which negatively affect plant water status. For instance, high leaf-to-air VPD may increase water loss via transpiration, leading to plant dehydration. Leaf trichome density of Cucumis sativus increased when air humidity decreased from 90 to 20% at 28°C, causing leaf-to-air VPD to increase from 0.4 to 3.0kPa . Shibuya et al. did not investigate cell or leaf expansion of C. sativus, but increased leaf-to-air VPD may promote trichome density because rising leaf-to-air VPDs reduces cell size , making space between trichomes smaller. In this study, higher leaf-to-air VPD and smaller leaves were observed when S. ×utahensis plants grew at the lower θtand the smaller epidermal cell size resulted in greater trichome density. Therefore, because increased leaf-to-air VPD and drought led to a reduction in cell enlargement and denser trichomes in S. ×utahensis, leaf trichome density was regulated using turgor-pressure-driven cell expansion to acclimate to drought conditions.Modern plant trade disturbs historical ecological relationships and creates opportunities for the development of novel pathogenic interactions , often with correlated genetic changes . However, pathogens must be adapted to the environment of the novel host before they meet, or they will not be able to survive and reproduce . That does not mean pathogens necessarily pre-adapted to the exact same host, but either could have adapted to a similar host earlier and retained that adaptation until encountering a novel host. Convergent evolution in diverse pathogen populations can allow for divergent strains to have the ability to infect the same hosts. Three potential mechanisms of genetic change that can accompany host shifts are nucleotide changes leading to different alleles in the core genome of a pathogen , whole gene gain and loss in the pan-genome, leading to unique sets of genes in individual strains, or regulatory/epigenetic changes. Due to the recent increase in whole genome sequencing of plant pathogens, we can now more effectively use phylogenetic analyses to investigate their genetic associations to both novel and historical host plants . Understanding the phylogenetic relationships between specific host and pathogens should allow the development of preemptive plans to protect natural ecosystems as well as agriculture from the emergence of novel pathogens. Xylella fastidiosa is an insect-transmitted, xylem-limited bacterial plant pathogen found across the Americas, and as of recently, globally. X. fastidiosa is considered to be a generalist pathogen, because, as a species; it reportedly infects at least 563 species belonging to 82 botanical families . The lack of host specificity that X. fastidiosa exhibits as a species contrasts with increased plant host specificity in smaller clades and strains . It is still debated whether a pathogen like X. fastidiosa should be considered a generalist species that “leaps” between phylogenetically distant hosts or, alternatively, a crawler at shallower clades . The difference is biological as there are unique implications for either evolutionary path. X. fastidiosa could be repeatedly evolving specialization or it could have biological and genetic traits as a species that make particular hosts of disparate plant taxa suitable. From an applied perspective, there have been recent calls from government agencies for increased focus on understanding the host range of X. fastidiosa. This is because the pathogen has been deemed likely to spread and to be of extremely high risk to crops of agricultural value , 2015. Xylella fastidiosa causes disease in a range of high value crops, including Pierce’s disease of grapevines, citrus variegated chlorosis disease in sweet oranges, almond leaf scorch, leaf scorch of coffee, olive quick decline syndrome , spanning North and South America, Europe, the Middle East, and Taiwan .

Dry soils also inhibit reproduction, survival, dispersal, and development of symptoms in host plants . Soil moisture conditions experienced by the pathogen themselves arise from interactions among the precipitation regime, soil depth, drainage, and atmospheric evaporative demand, and thus reflect the interplay of edaphic and climatic conditions. Finally, Pc disease is also often suppressed in rich soils where organic matter content exceeds
5% , probably because of predation by other soil organisms in the diverse microfaunal communities sustained in these soils . Projections of potential future risk therefore require techniques to assess the impact of multiple environmental changes and their interactions on pathogen range and epidemiology. Here, we apply a mechanistic modeling approach to explore how climate change could impact pathogen range and activity. We explore how simultaneous changes in temperature, precipitation, snow-pack extent, and evaporative demand might impact the range of a well-characterized pathogen under different climate scenarios. To do this we couple two existing models that describe controls on the range of the generalist root pathogen Pc in the state of California and surrounding regions in the states of Oregon, Nevada, and Arizona in the southwest USA. Pc occurs in this region but its range is poorly delineated. In other warm climates such as southern Australia and Hawaii, Pc has had a devastating effect on timber production, natural forests and agriculture € .

Modeling the climatic and edaphic limits on its potential range in the US southwest will help determine the risks posed by this pathogen, procona florida container particularly since there is not as yet a detailed understanding of the susceptibility of native species to Pc infection.To address the research questions, we link two existing models that describe: Pc winter survival probabilities and Pc disease severity during the spring growing season . We first model winter soil temperatures. Second, we use the modeled soil temperatures to estimate Pc survival with an existing, validated survival model . Next, we expand a probabilistic soil moisture model to account for snowpack contributions. We use the projected soil moisture fluctuations to drive a stochastic pathogen risk model, previously employed to describe disease range, relative risk on different soil types, and response to different irrigation regimes at sites in Western Australia and Oregon . The output of this model is a metric of the likelihood of disease expansion throughout a hosts’ root system, lying between 0 and 1 . Fourth, we identify regions with high soil organic content from the national State Soil Geographic Database and Soil Survey Geographic Database data sets and exclude these regions from the potential Pc range . Finally, we compute a relative Pc risk as the product of the risk of winter survival, absence of suppressive soil conditions, and moisture-controlled host colonization. We apply this modeling framework to baseline and future climate scenarios across the southwest USA. We repeat the analyses using high resolution downscaled climate and soil data for the San Francisco Bay Area , allowing us to explore the effects of microtopography, orography, and fine-grained changes in soil properties, typical of the California coastline, on the spatial patterns of Pc disease risk and its projected climate sensitivity.We use three different climate datasets: monthly regional historical climate observations interpolated to a 140-km2 grid for the 1950–2000 period ; bias-corrected and spatially downscaled National Center for Atmospheric Research Community Climate System Model 3.0 monthly simulations run for the IPCC A2 and B1 climate scenarios on the same 140 9 140 km grid ; and 30-year average monthly projections from the Geophysical Fluid Dynamics Laboratory climate models for the same scenarios, downscaled to a high resolution grid over the San Francisco Bay area for the 1970–2000 and 2035–2065 periods.

To obtain representative climate characteristics, we average the monthly regional data over 10 year intervals . The climate components obtained for each dataset are summarized in Table 1. We estimate average daily temperature for the Bay Area climate surfaces as a simple mean of monthly maximum and minimum temperature; and estimate the potential evaporation at the southwestern US scale using the Priestly–Taylor equation. One of the limitations of this approach lies in the considerable uncertainty surrounding the use of downscaled climate datasets. Our goal in using these high resolution data sets was to: develop a reasonable baseline drawn from interpolated observations and compare this against plausible climate futures to elucidate the range of potential Pc responses and how they could manifest themselves at regional scales. We do not interpret these future cases as predictions or forecasts, but illustrative scenarios to offer insight into process interactions. Regional soil data, specifically percentage clay, organic matter content and soil depth data are derived from STATSGO . Both data sets derive from soil maps with irregular mapping units ranging from <1 ha in size to >20 000 ha . The soil data were thus mapped to the climate grid scales used for both the Bay Area and the regional scale. We use tabulated hydraulic parameters to estimate the properties of the water retention curve for each soil type on the basis of the percentage clay content, classifying <10% clay soils as sands, 10–15% clay content as loamy sands, 15–20% clay contents as sandy loams, 20–40% clay content as loams and >40% clay contents as clays. Georeferenced locations where Pc has been isolated from natural or agricultural soils in California were obtained from the Phytophthora Database , Forest Phytophthoras of the World , and recent literature sources .The southwestern USA contains a climatically, geologically, and biologically diverse set of landscapes . Climatically, the region encompasses cool, humid, oceanic climates near the coast, arid interior valleys and deserts with continental climates, and montane and nivial uplands in the Sierra Nevada mountain ranges. Precipitation tends to be highly seasonal with the majority falling during a 4-month period from December to March. The diversity of climatic types is reflected in the diversity of terrestrial vegetation, with important vegetation communities including coastal rainforests, dry chapparal, Mediterranean oak savannas, montane forests, and subalpine regions . Within the study area, the California Floristic Province is recognized as a biodiversity hotspot , and the state is home to over 7600 plant species . The San Francisco Bay Area, which is used as a higher resolution case study maintains much of the same complexity of climate, geology, and biology, due to strong ocean-inland gradients in temperature and annual precipitation , and the activity of various tectonic faults in the region . The diversity of climate and soil types in the study area can be directly parameterized within the stochastic soil moisture and pathogen models. Note that we have allowed spatial variations in evaporative demand to be driven by radiation, temperature, and humidity , and have not incorporated an explicit treatment of varying vegetation cover on Emax. The rationale for this lies in the fact that surface conductance loses sensitivity to LAI for low humidity and LAI > 2, suggesting that the first order controls on Emax variation lie in energy limitation rather than vegetation characteristics . The diversity of the vegetation also presents a challenge for parameterization of the pathogen dynamics in the model. It is clearly not feasible to parameterize specific host resistance terms for Pc for all 7600 vascular plant species in the study area; furthermore assessments of susceptibility of the native vegetation to Pc are strikingly incomplete, and remain insufficiently quantitative to allow a direct comparison to be made. The problem is similar to applying this model to Pc in Western Australia, where far more work on Pc susceptibility has been performed. In that comparably biodiverse landscape, 3084 of the 5710 native plant species were found to be susceptible or highly susceptible to Pc infection . This determination, however, required intensive surveys of native vegetation in representative habitats, as well as comparative inoculation greenhouse trials , and still provides only qualitative information about the relative vulnerability of host plants. No comparable investigations have as yet taken place in the US south west, procona London container although numerous common native species are known to be vulnerable to Pc, including manzanita species , chapparal species , MonTherey pine, sycamore, western sword fern, coast live oak , bay laurel and madrone .

In the absence of detailed observations of host–pathogen interactions for the diversity of species in Western Australia, we explored climate limitations on range using a combination of: laboratory estimates of Pc growth rates in sterile media and host resistance parameters taken from a moderately susceptible host and estimated to be 0.1 day 1 . We have repeated this approach for the US southwest with the following rationalizations: moderately susceptible host dynamics ensure that climate effects are clear. For highly susceptible hosts, climatic drivers are relatively unimportant, since any serendipitous infection is likely to lead to mortality. For unsusceptible hosts, climate is equally unimportant. Thus to explore the impacts of climate change, moderately susceptible host assumptions are sensible; Evidence from infections of common tree species suggests that the hosts are indeed moderately susceptible to Pc, with infections most common in wet areas and mortality commonly associated with concurrent pest or drought stress ; for the sake of identifying climate sensitivity specifically, holding host vulnerability constant across the study area provides a useful control, and in the absence of more detailed information, is the most parsimonious modeling approach. As a check on the impacts of this assumption, we have also evaluated the sensitivity of the model results to a doubling or halving of host resistance.The effects of a warming climate on Pc risk vary depending on the risk factors and specific climate scenario being assessed. Warming changes winter survival in a straightforward fashion: under the A2 scenario survival increases dramatically so that the region in which more than half of a Pc population would survive the winter increases from 43% to 72% of the study area. More modest increases in winter-survival probabilities arise under the B1 climate scenario, in which Pc winter survival becomes probable over 65% of the study area . The effects of climate change on soil moisture and Pc spring activity are more complex. At the regional scale, climate change reduces the risk posed by Pc across the majority of the study area. However, the changes are spatially variable. Pc risk declines markedly in the Central Valley. It is largely unchanged in coastal northern California and Oregon, where rainfall levels are projected to remain high. Its range is also unchanged in the south-eastern part of the region, which is significantly water limited under contemporary scenarios and projected to remain so. Pc risk increases in the north-eastern extent of the study area. The increase in Pc risk in this area is greatest in the high emissions scenario. In the lower emissions scenario, comparable increases in Pc extent occur inland in the southern extent of the range. In both cases, these increases indicate an interaction of warmer temperatures with unchanged or slightly enhanced rainfall. The potential complexity of the interactions between changing water and temperature in one of these southern locations is illustrated in Fig. 5 for a site in the southwestern part of the region. In this location, Pc risk increases under the B1 scenario but decreases under the A2 scenario. Figure 5 shows a decomposition of the projected changes into those due to temperature and those due to changes in soil moisture. As shown, increasing temperatures increase Pc risk from the baseline case for both A2 and B1 scenarios, but in the A2 scenario, a decrease in soil moisture more than offsets the effect of warmer spring conditions. Conversely, under the B1 conditions, the slight increase in soil moisture increases pathogen risk at this location, but only when both temperature and soil moisture increase together does the large predicted increase in pathogen risk occur. While these threshold-dynamics are not general across the study range, they illustrate the potential for highly nonlinear pathogen responses to interactions in changing temperature and moisture conditions, and highlight the importance of considering the impacts of synchronous changes in climate on pathogen dynamics. As summarized in Table 2 and illustrated in Fig. 2, water limitation reduces Pc risk over a range of 340 000 km2 across the region for the high emissions scenario, with an average decrease in the Pc risk of 0.28. Pc risk is reduced over 40 000 km2 for the low emissions scenario, by 0.26 on average.

A second related question concerned the relationship among the recombinant IHR group members. In particular, what could be concluded about the origin of the group given the observation from 2 independent studies that the members appear to form a well-defined cluster of genotypes? Third, we used the sequence data to examine the hypothesis that the introgressed DNA was from X. fastidiosa subsp. fastidiosa, the subspecies that causes Pierce’s disease. X. fastidiosa subsp. fastidiosa is native to Central America, and all known isolates in the United States and northern Mexico can be traced back to a single introduced genotype . IHR would be of limited interest if it simply randomized the genetic differences among the subspecies but had a minimal effect on pathogenesis. For this reason, we were particularly interested in documenting any possible invasion of new plant hosts associated with IHR. The hypothesis is that IHR creates a range of novel genotypes that are far more variable than can arise from a lineage diversifying through point mutations, and this diversity facilitates adaptive evolution of a kind not possible for a clonal lineage. This kind of probabilistic evolutionary hypothesis can rarely be directly proven based on an individual case; however, it makes predictions that, if generally supported, would cause the hypothesis to be accepted. In the case of X. fastidiosa, plastic planter pot compelling evidence supporting the hypothesis would be the invasion of a new native host plant that is uniquely associated with IHR. Our data support this hypothesis: in X. fastidiosa subsp. multiplex, IHR is indeed associated with the invasion of at least 2 new native plant hosts, blueberry and blackberry.

To investigate intersubspecific homologous recombination , we analyzed 31 isolates previously identified as IHR-type and 2 isolates previously identified as intermediate-type X. fastidiosa subsp. multiplex , based on sequence of the 7 housekeeping loci used in the MLST scheme defined by Yuan et al. plus a region of the pilU gene. Together, these 33 isolates made up the recombinant group. Details regarding the isolation and typing of the 33 isolates were provided by Nunney et al. , and a summary of salient features is provided in Table S1 in the supplemental material in that article. All sequences used have previously been published and are available both in GenBank and the MLST website . To detect IHR, we employed a modified version of the introgression test developed by Nunney et al. . In its original form, the test compares a set of target sequences, some of which may have been involved in IHR, to a set of potential donor sequences. Each variable site is classified as F, a fixed difference between the target sequences and the donor sequences, or P, a polymorphic site within the target sequences where at least one variant base is shared with the donor set. In the modified version of the test, the targeted introgression test, the target sequence is known a priori and is compared to two references, the donor group, D , and the ancestral group, A . The minimum number of nucleotide differences between the target and the two references defines a ratio of D to A equivalent to the ratio of F to P and can be tested in the same way . In some cases, there is no breakpoint because the whole locus appears to be an introgressed sequence . Although the signal of introgression across the entire sequenced region may be clear, it is valuable to have a statistical test that documents the strength of the signal. In this case, the null expectation is the ratio that reflects the pairwise differences between the donor and ancestral group versus the pairwise differences within the ancestral group .

We used this ratio to define the expectation of the D/A ratio for a chi-square test of complete introgression. Gene diversity and distance trees were calculated using MEGA5 , and the maximum parsimony tree was created using the PARS program in Phylip . Distance trees and the maximum parsimony tree were used rather than other methods, given the known occurrence of intersubspecific recombination in the data. ClonalFrame was used to provide an independent estimate of the relative importance of recombination versus mutation in the recombinant group.Based on 8 loci sequenced , Nunney et al. identified 9 sequence types belonging to the recombinant group of X. fastidiosa subsp. multiplex. These STs all showed evidence of intersubspecific homologous recombination at one or more of the 8 loci and were characterized by 18 alleles, 10 of which were never found in non-IHR X. fastidiosa subsp. multiplex strains . These 10 alleles were examined for evidence of IHR by comparing them to the previously described non-IHR X. fastidiosa subsp. multiplex alleles and to the known X. fastidiosa subsp. fastidiosa and sandyi alleles . Of these 10, 4 alleles were found to be derived in their entirety from X. fastidiosa subsp. fastidiosa, and 3 were found to be chimeric for X. fastidiosa subsp. multiplex and fastidiosa sequences, with significant evidence of one or more recombination breakpoints. These 7 alleles encompassed 4 loci: leuA, cysG, holC, and pilU. The locus most strongly implicated in IHR wascysG, since all of the 9 recombinant-group STs were characterized at this locus by 1 of 3 cysG alleles unique to the group. The involvement of IHR in the genesis of all 3 of these alleles is illustrated by their close genetic relationship to X. fastidiosa subsp.fastidiosa and sandyi alleles . Allele 12, apart from being found in the recombinant group, is an X. fastidiosa subsp. fastidiosa allele . The other two alleles were found to be chimeric: allele 18 contains a single recombinant region at the 3= end of 342 bp, while allele 6 has two short recombinant regions, one at the 5= end of at least 23 bp and another toward the 5= end of at least 35 bp .

The DNA sequence variation defining these patterns is shown in Table 2. The patterns seen in the DNA sequences of the 3 cysGalleles are consistent with the hypothesis of a single IHR that introgressed donor allele 12 into X. fastidiosa subsp. multiplex, followed by subsequent intrasubspecific recombination reintroducing X. fastidiosa subsp. multiplex sequence to create alleles 6 and 18 . There are no inconsistent sites, provided the 5= recombination breakpoint in allele 18 starts after position 71. Introgression of X. fastidiosa subsp. fastidiosa sequence into X. fastidiosa subsp. multiplexwas found in alleles at 3 other loci . In the case of pilU, 7 of the 9 recombinant STs carried either an allele identical to a known X. fastidiosa subsp. fastidiosa allele or 1 bp different from it . Allele 1 is an allele that characterizes most U.S. isolates as well as several STs found in Costa Rica, while allele 9 is unique to the recombinant group. The leuA locus has a single statistically significant recombinant allele, allele 4 . It differed by 2 bp from the X. fastidiosa subsp. fastidiosa allele 9 but by 8 bp from the most similar nonIHR X. fastidiosa subsp. multiplex allele. X. fastidiosa subsp. fastidiosa allele 9 could be the donor for its entirety , although if the recombination region started after site 10 but before position 520 , then only one site would be unexplained. That remaining site carries a base unique to this allele and is probably a novel mutation. If the recombination breakpoint was 3= of position 295 then X. fastidiosa subsp. fastidiosa allele 1 provides as good a match as allele 9 . Similarly, 30 litre plant pots holC allele 7 was also 8 bp different from the most similar non-IHR X. fastidiosa subsp. multiplex allele, providing clear evidence that the 5= end was derived from X. fastidiosa subsp. fastidiosa . The pattern can be explained if X. fastidiosasubsp. fastidiosa allele 19 is the donor of the 5= region ending somewhere between positions 183 and 286, since it leaves no inconsistent bases .Evaluation of the plausibility of a single initial IHR event is complicated by the possibility of subsequent intrasubspecific recombination both within the recombinant group and between the recombinant group and the dominant non-IHR X. fastidiosa subsp. multiplex strains. Plausible sets of recombination events were determined by creating a tree using maximum parsimony applied to the 10 8-locus genotypes . Using allele numbers as characters, there were 2 equally parsimonious trees, each with 14 steps. They differed only in the precise positioning of 22a ; however, assuming a basal introgression of pilU1, only the tree shown in Fig. 3 remained. The hypothetical donor and recipient genotypes were added to root the tree, with the tree dictating gltT3 in the ancestral recipient genotype.

The most parsimonious tree showed that the pattern of introgression was more complex than could result from a single IHR. There are four main events that illustrate this complexity. First, based on this tree, the grouping of STs 27, 28, and 40 is defined by the introgression of holC7, a recombinant allele introduced into the tree far from the basal recombination event. Second, although the mutation of pilU1 could explain the appearance of pilU9, a second introgression of pilU1 would be necessary to account for its appearance in STs 28 and 40. Third, a number of events are necessary to account for the evolution of the cysG locus. While cysG12, an X. fastidiosa subsp. fastidiosa allele introduced in the initial recombination event, could give rise to cysG18 by the introgression of X. fastidiosa subsp. multiplex sequence , this allele appears in two places in the tree, necessitating a lateral transfer within the recombinant group. Despite this complexity, the hypothesis of a single primary IHR event creating the founder of the recombinant group is strongly supported by the pattern seen at the cysGlocus. As noted above, all members of the recombinant group share one of 3 alleles that appear to be derived from a single introgression of donor allele 12. Analysis of theX. fastidiosa subsp. fastidiosa donor.The proposed X. fastidiosa subsp. fastidiosa donor is defined at 4 of the 8 loci: leuA9, cysG12, holC19, and pilU1. Of these 4 alleles, only pilU1 was found in an extensive genetic survey of 86 isolates of X. fastidiosa subsp. fastidiosa within the United States and northern Mexico . The results of this survey, combined with similar genetic data from Costa Rica, led to the conclusion that all isolates of X. fastidiosa subsp. fastidiosa found in North America were derived from a single ancestral strain introduced from Central America . Consistent with this hypothesis was the observation that, in the North American isolates, no allele at the 7 MLST loci orthe pilU locus was more than 1 bp different from the most common allele. Given this background, we can examine the hypothesis that the proposed ancestral donor is consistent with theX. fastidiosa subsp. fastidiosa strains currently found in the United States. Similarly, at leuA there is no inconsistency with U.S. allele 1 if the recombination breakpoint in the recombinant allele 4 was after position 295 . If the breakpoint is before that point, then Costa Rica allele 9 provides a better fit of only 1 bp, a minor difference. In marked contrast, the alleles cysG12 and holC19 have only been found in Costa Rica, not in the United States , and differ markedly from the U.S. alleles. In particular, within the IHR regions, the U.S. X. fastidiosa subsp. fastidiosa alleles cysG1 and holC1 are 5 and 7 bp different, respectively, from the recombinant group sequence, while the Costa Rica alleles precisely match the donor sequence . These large differences require us to reject the hypothesis that the primary X. fastidiosa subsp. fastidiosa donor was derived from the introduced genotype that was the ancestor of all of the North American X. fastidiosa subsp. fastidiosa isolates that have been typed. Estimating recombination rates in the recombinant group of X. fastidiosa subsp. multiplex. The prevalence of recombination over mutation in the evolution of the recombinant group was supported by a ClonalFrame analysis: the estimated ratio of recombination events to mutation was 19,310, with a 95% confidence lower bound of 45.3. Addition of the potential ancestor and/or potential donor genotypes to the analysis maintained high estimates of the lower bound of / . These lower bounds are high for a largely clonal organism, and they illustrate the pervasive involvement of recombination in the genesis of the recombinant group.

In particular, we highlight the combination of independent and particularly joint effects of climate and soil on trait variation, an interaction that has to date been neglected because few studies include both in a single analysis, at the global scale as we have done here. In doing so, we identify an important gap in knowledge: what is the nature of climate–soil interactions that drive whole-plant trait variation and what distinguishes the majority of climate and soil factors having joint effects on plant traits from those with independent effects? These are the sorts of questions that require answers to increase our capacity to predict plant functional diversity in a changing environment. Such predictive power would contribute to a sound basis for assessing long-term feedbacks between global environmental change and the terrestrial biosphere, helping to constrain parameters of global coupled climate–vegetation models. Humans are currently modifying both climatic and edaphic conditions at the global scale. Climate envelope models used to predict vegetation shifts must be complemented by drivers related to large-scale anthropogenic alterations of soil conditions resulting, for example, from land-use change, atmospheric nitrogen deposition, fertilization, black plastic nursery pots liming and salinization. Our global analysis provides an essential context for finer-scale studies to directly tackle questions of biological processes and mechanisms at landscape and community scales.

Fire is part of the natural disturbance regime of many boreal regions, although recent evidence suggests that anthropogenically induced climate change may be increasing the burned area in North American and Eurasian forests . Because high-latitude ecosystems store approximately 40% of global carbon stocks in biomass and soils , an amount equal to the atmospheric C pool, there has been considerable inTherest in understanding how these systems will respond to climate warming. Combustion of vegetation and forest floor transfers C directly from terrestrial ecosystems to the atmosphere, so increased burned area or fire intensity in the boreal biome could be a strong, positive feedback to atmospheric CO2 concentrations , at least in the early years after fire . At a regional scale, the effect of fire on species composition, soil drainage, and stand age distribution will ultimately regulate whether the CO2 feedback is positive or negative. The response of this long-term signal to the combination of climate change and alThered fire regime is largely unknown for the boreal biome. Patterns of plant species composition, biomass accumulation, and productivity across post-fire succession are important determinants of the amount , structure , residence time , and decomposability of C inputs to these systems. In Interior Alaska, black spruce BSP stands cover approximately 70% of the forested area and occupy landscape positions that range from permafrost-free well-drained soils to permafrost dominated and poorly drained soils . These forests are highly flammable due to their architecture and resin production as well as the thick moss layer on the forest floor , and fire return intervals range from 70 to 100 years . Wildfires tend to be large and high-intensity and while few boreal overstory species survive fire, many understory species re-sprout after fire from buried or protected meristems .

Black spruce are semi-seritonous and release seed after fire, with the majority of trees recruiting in the first 5 years after fire . Stands may or may not go through a deciduous phase where trembling aspen and tall shrubs , which may resprout after fire , dominate over the 15–50 years prior to closure of the black spruce canopy . Moss and lichen expansion across the forest floor follows similar timing, with moss cover reaching its maximum between 30 and 50 years , concurrent with canopy closure by black spruce and a reduction in deciduous litter. The deciduous phase appears to be related to interactive effects of fire severity and site drainage as evidenced by the fact that sites that burn severely , or at a high frequency have the highest abundance of aspen and willow species. At the landscape level, both the severity and frequency of fire appear to be related to soil drainage . Although patterns of species dominance over post-fire succession have been described for Alaskan black spruce stands, there are few published measurements of productivity and biomass after fire. In conjunction with a chronosequence study of soil C dynamics, O‘Neill and others used a mass balance model to conclude that C inputs balanced C losses 7–15 years after fire. Yarie and Billings used forest inventory data from stands across Alaska to estimate generalized biomass accumulation curves for black spruce green timber. They show that biomass accumulation peaked between 75 and 150 years. Simulation modeling of ecosystem C dynamics over post-fire succession suggests that C balance is most sen-sitive to N fixation, moss accumulation, organic layer depth, soil drainage, and fire severity.

Finally, there are several comprehensive studies of post-fire succession in Central and Eastern Canada , but the trees in these sites inhabit different soil drainage and temperature regimes than their Alaskan relatives, potentially resulting in different rates of ecosystem C dynamics . The goals of this study were to describe the changes in community structure and above ground net primary productivity and biomass that occur over post-fire succession in the upland black spruce forests of Interior Alaska. We present measurements that span two different time scales: recovery 1–4 years after fire and recovery over the entire successional cycle. For the former, we followed vegetation recovery for 4 years after the 1999 Donnelly Flats fire near Delta Junction, Alaska. We used a chronosequence approach for the latter by selecting two sequences of sites in the region that varied primarily in time since fire: a mesic sequence on moderately well-drained soil with permafrost and a dry sequence located on well-drained soils without permafrost . These sequences represent transitions in environmental factors that might occur with climate warming, including loss of permafrost and subsequent increases in soil drainage .This study was conducted in the Donnelly Flats area located near Delta Junction in Interior Alaska, in seven upland sites that were previously dominated by black spruce . All sites were located within a 100-km2 area on gently sloped alluvial flats that range from moderately well-drained soils dominated by permafrost to well-drained soils where permafrost was largely absent. Our study include three sites on well-drained soils that burned in stand-killing wildfires in 1999, 1987, and approximately 1921 , hereafter the dry chronosequence, and four sites on moderately well-drained soils that burned in 1999, 1994, 1956, and approximately 1886 , hereafter the mesic chronosequence. Time since last fire was determined by historical record in the younger sites and by tree ring analyses in the older sites. Some or all of these sites have been used to assess the effects of fire on soil C storage and emissions , soil chemistry , hydrogen fluxes , fungal community composition and dynamics , seasonal CO2 and 18O–CO2 fluxes and energy exchange . Within each chronosequence, sites were chosen to have similar state factors other than time . Climate: Micrometerological data collected in the 1999, 1987, and 1921 dry sites and the 1994 , 1999 and 1886 mesic sites support the idea that sites in both chronosequences experienced a similar climatic regime. The regional climate is cold and dry with an annual mean surface air temperature of 2.1C during the 1970–2000 period . Over this same period, mean temperatures in January and July were 20 C and 16.0C, respectively, and mean annual precipitation was 290 mm. Approximately 65% of precipitation fell during June, July, and August. Potential biota: Although all stands were currently or historically dominated by black spruce and were in a close enough proximity that they belong to the same regional pool of potentially colonizing organisms, 30 plant pot the understory vegetation and ground cover varied with soil drainage and stand age .

The oldest dry stand was a lichen woodland , with ground cover dominance split between feather moss and lichens. Vaccinium uliginosum and V. vitis-idaea were the most abundant understory species, with deciduous shrubs and trees, forbs and graminoids present but at low abundance. Many of the same species resprouted or recruited after fire in the 1999 dry site and dominated the understory in the 1987 dry site. Species characteristic of well-drained ecosystems that were present in all dry chronosequence sites and absent from the mesic sites were the grass Festuca altaica and the evergreen shrub Arctostaphylos uva-ursi. These species were present, however, on trails and roadsides around the mesic sites. The oldest mesic stand had continuous feathermoss ground cover and a high abundance of Vaccinium spp. Feathermoss occupied almost the entire ground surface in the 1956 and 1886 mesic sites. In the 1994 mesic site it persisted in patches that appeared to have escaped burning. Vascular nomenclature follows Hulte´n and non-vascular nomenclature follows Vitt and others . Relief: Sites in both chronosequences were within a 100- km2 area with little variation in slope or topography . Parent material: Soils along both chronosequences were mainly derived from the Donnelly moraine and wind blown loess and have been described in detail elsewhere . Differences in drainage between the chronosequences are thought to be related to differences in water table depth and texture . Although great care was taken to control state factors within and between chronosequences, it was difficult to fully constrain the effects of past fires on productivity or biomass pools. In the 1999, 1994, and 1987 sites, fires were stand replacing . In the 1957 mesic site, the relatively small range of tree sizes suggests a single cohort of black spruce. In the mature 1886 mesic and 1921 dry sites where tree sizes are quite variable, however, the number of trees sampled for age was not large enough to determine whether stands are comprised of a single cohort . At the landscape-scale, the severity and frequency of fire are likely to be related to soil drainage . At the site level, however, stochastic factors such as weather conditions, time since last fire, and neighboring vegetation can also affect fire severity. Post-fire vegetation recovery is similarly affected by stochastic processes such as timing of fire in relation to both vegetative and reproductive phenology, proximity of seed source, and/or the effects of past and present climate conditions on demographic processes. Finally, we caution the reader to keep in mind at all times that this is an observational study; we depend on the assumptions of the chronosequence approach to make inferences about time.Above ground biomass of vascular plants, mosses and lichens was measured across all sites by destructive harvest in July 2001 at approximately peak biomass. To more closely examine the dynamics of regrowth in the first several years after fire, biomass was also measured in the 1999 dry site 2 months after the fire as well as mid-summer in 2000–2002; it was also measured in 2000, 2001, and 2002 in the mature dry site for comparison. Trees less than 1.37 m in height that were excluded from the inventory described above were included in these harvests. In harvests of the 1999 dry site, we determined whether each species was a re-sprouter by assessing the presence of charred stems or large rhizomes. We also monitored species or generic richness on an annual basis in these sites by recording the presence of all species within a 144-m2 plot surrounding the 1 m2 harvest blocks. We did not survey species richness in the other sites. In each site, above ground biomass was clipped from either 6 or 12 randomly located 1 m2 quadrats. Organic depth was measured at the four corners of each quadrat and averaged. In the mesic chronosequence sites and the 1987 dry site, above ground biomass of vascular species was clipped from six 1 m2 quadrats randomly located along two 100 m long permanent transects . Mosses and lichens were collected from a 400-cm2 organic soil plug sawed from a randomly selected corner of the 1 m2 quadrat following vascular plant clipping. In the 1987 dry site and the 1994 mesic site, we also harvested tall shrubs in a 4-m2 quadrat surrounding the 1-m2 quadrat to account for their larger stature. Vegetation was harvested similarly in the 1999 and 1921 dry sites, except that 12 quadrats were harvested. Samples were returned to the lab and sorted into species and tissues within 1 day of harvest. Each vascular species was separated into several tissue categories including current year and previous year leaves, current year and previous year stems, and fruits or inflorescences following methods modified from Shaver and Chapin and Chapin and others .

Individual-level daily egg laying data for three fruit fly species were used for the analysis for the two tephritid fruit fly species including the Mediterranean fruit fly , commonly known as the Medfly , the Mexican fruit fly , commonly known as the Mexfly , and the vinegar fly, Drosophila melanogaster . Husbandry details for each species are described in the above-cited papers. In brief rearing conditions were 25-27° C and 50-75% RH, and 12:12 L:D for the two tephritid species and 10:14 L:D for D. melanogaster. Whereas the two tephritid species were fed a 3:1 sugar-yeast hydrolysate diet, D. melanogaster females were fed a standard agar-gelled Drosophila food medium. Eggs were collected from mesh at one end of the individual cages for the tephritid females and from the food medium for D. melanogaster . These species were chosen primarily for the availability of databases on individual-level lifetime egg laying. However, they were also chosen because they allowed for three levels of comparison to test the robustness of our discoveries—between two dipteran families , between two tephritid genera , and among three different species.Reproductive data were excluded for the first 10 days of adult life for each of the species, a period during which all individuals matured and developed their first eggs. The remaining data for each fly were then parsed into 1-of-2 segment categories: Terminal segment consisting of the sequence of daily reproduction from 11 days before death to and including the day of death. There were 873, 1,071 and 425 terminal segments derived from individual female Medflies, raspberry containers size the Mexflies and D. melanogaster, respectively; and Midlife segments of the same length. The midlife segments were created from contiguous 11-day series of individual flies that started after maturation and ended 11 days before the fly died.

This selection criteria thus excluded individual flies that lived less than 32 days . This segmentation process was repeated until the number of remaining days to the terminalsegment was less than 11. The remaining days were ignored. The 11-day egg-laying sequences were chosen primarily because the results of preliminary investigations revealed that 11-days was the shortest period of egg laying that contained patterns that yielded consistently high performance metrics across all three species. This period was also chosen because longer egg-laying periods would have substantially reduced the number of segments in each of the databases. We computed two metrics from all of the 11-day egg laying sequences for all three species and the terminal and mid-life segments: 1) Total eggs . This metric was chosen based on the observation that most flies laid fewer eggs at the end of life; and egg-laying ratio . This metric was based on the observation that the relative rate of egg laying in most flies decreased at the end of life. This metric was computed as the ratio of the number of eggs produced from 11 to 6 days prior to death to the number of eggs produced from days 5 to 0 prior to death. This ratio specified both a direction and a magnitude of change as an individual fly approached death. Egg ratio was assigned a value of ER=0 if no eggs were laid in either time interval and a value of ER=5.0 if eggs were laid in the first 6 days but none in the last 5 days—a pattern that suggests rapid egg-laying decline to zero but that results in a zero in the denominator . This value was chosen because it was at the mid-range of the highest ER-values when eggs were laid in both first and last segments. This ER value was high enough to serve as a major change metric between the first and last egg-laying subsegments but not so high to be the overriding drivers of the statistical outcomes.Fig. 1 shows the egg laying patterns of selected individual medfly females at 10 different life table deciles.

With the exception of female #5, the egg laying patterns during the terminal segments were consistent with the hypothesis that rate of egg laying near the end of each of their lives was both low and decreasing . However, the egg-laying patterns that characterize the end of female’s life are also sometimes observed at times when they are not approaching death. Indeed, there are also a number of 11-day midlife sequences of some flies that are indistinguishable from these same patterns in the terminal phases. For example, egg laying rates decrease in fly #5 from days 20 to 30 and in fly #8 from days 30 to 40 days. Although these decreasing egg-laying patterns usually predict impending death, both of these flies another 11 days beyond these ages. Similar egg-laying trends are also evident during the midlife of fly #9. Fly #10 produced very few eggs for a 40-day period from ages 20 through 60 days. Thus this visual inspection of egg laying in 10 different individuals reveals the statistical challenge of classifying egg-laying sequences as either terminal or mid-life.Although computing all three of these parameters is straightforward in both chronological or thanatological age, the values for and interpretations of GRR and T differ between the two age categories. Consider the following hypothetical case for clarifying the differences. Suppose that in chronological time, the age-specific egg laying in four female fruit flies was identical for the ages each were alive with reproductive peaks at 50 eggs/day on day 20. However, they each die at different ages 20 days apart—i.e., at ages 20, 40, 60 and 80 days. When their reproductive schedules are considered in thanatological ages, which is to say, relative to their age of death rather than their age of birth , then their reproductive peaks are in thanatological time correspond to ages 0, 20, 40 and 60 days . Although R0 remains the same since every female still produces the same lifetime number of eggs, the values of both GRR and T will in the vast majority of cases be different because the timing of reproduction is relative to age of death rather than to age of birth .

Whereas day 20 was the average age of peak reproduction in chronological time for the hypothetical example, it is day 30 in thanatological time [i.e., /4].A 2×2 contingency table that classifies prediction is shown in Fig. 2 where the numbers along the major diagonal represent the correct decisions made, and the numbers in the other diagonal represent the errors . If the instance is positive and the instance is a positive, it is counted as a true positive. However, if it is classified as a negative it is a false negative. If the instance is negative and the instance is a negative, it is counted as a true negative. However, if it is classified as a positive it is a false positive. This contingency table forms the basis for a number of common metrics given below.Event-history reproductive charts plotted in both chronological and thanatological time for all three fruit fly species are presented in Fig. 3. Several aspects of these graphs merit comment. First, the chronological plots of egg laying patterns for all species reveal the familiar progression starting at eclosion from the pre-reproductive, raspberry plant container maturation period, followed by a periods of high reproduction with relatively high levels of intra-individual and inter-species variability. This period, in turn, is followed by a period of tapering off, the length of which depends largely on an individual’s lifespan. The low levels of egg laying are evident at older ages in all species but is most striking in the oldest D. melanogaster. Second, the event-history plots in thanatological time reveal visually the repositioning of reproduction that occurs when the schedule is normalized with respect death rather than birth . This shift is especially evident in diagonal band of high reproduction that tracks to the left of the cohort survival the curve. In these cases the most advanced ages in thanatological time correspond the very youngest and thus most fecund ages in chronological time. Third, patterns of egg laying near death as seen in all three species and for both time frames differ between short-lived and long-lived individuals. This is outcome of the differences in the underlying “causes” of death at young and old ages. Increasing frailty due to old age is the most likely “cause” of death in the longest lived individuals. This accounts for the progressive decrease in egg production at older ages. However, increasing frailty due to aging is an unlikely “cause” of death for flies that die young and at ages when they are at or near their peak in egg production. Thus no single egg laying pattern or combination of patterns will likely ever apply to all flies regardless of age or cause of death.Comparisons of the average reproductive rates and timing for all three species plotted with respect to both chronological and thanatological ages are given in the different panels shown in Fig. 4. Because the number of eggs laid by the average female in her lifetime is the same regardless of whether the eggs are summed from birth to death or from death to birth, net reproductive rates, R0, will be the same regardless of whether it is considered in chronological or thanatological time. But as noted in Methods section, this not the case for gross reproductive rate computations.

For example, the 50% greater value of GRR for thanatological age relative to the value of this metric for chronological age in D. melanogaster is the result of a subset of extremely long-lived individuals who both matured early and produced many eggs at young ages. When re-plotted these young ages in chronological time represent the “old” ages in thanatological time. Thus individuals who are both long-lived and highly fecund at young chronological ages represent a large fraction of the small number at the tail end of the “death” cohort. Differences in the values of the mean age of parenthood, T, across species for chronological versus thanatological ages revealed that it was 8 days closer to birth than to death in D. melanogaster, but slightly over 4 days closer to death than to birth in the Mexfly and nearly equidistant from birth and death in the Medfly .The means and frequency distributions of the two independent variables we use in the regression model for each of the fly species are shown in the series of plots contained in Fig. 5. These graphs anticipate the outcome of the modelling results by revealing the differences between the metrics in the midlife segments relative to the terminal segments. There were striking differences in the metrics for each of the two categories of segments in D. melanogaster with nearly 5-fold fewer eggs and 3-fold greater average egg ratio in the terminal segments. The signs of the differences in the mean and overall distributions for the two tephritid species were similar but the magnitudes of the differences were much less. We thus anticipate more favorable performance metrics for distinguishing between terminal and mid-life egg laying patterns in D. melanogaster than in the two tephritid species.The logistic regression model yields three general results. First, the model’s overall performance supports the concept that the egg-laying patterns of total eggs and egg ratio as individual flies approached death are, in the majority of cases, distinctly different from those patterns over an 11-day sequence in middle of their lives. This wasevident in the performance metric of fraction correct —i.e., the FC-value for all species exceeded 0.64 using all data and exceeded 0.73 using only the segments in which flies laid 25 eggs or greater . Second, with a minor difference for several of the D. melanogaster metrics, the performance of the regression model was more favorable when applied to the censored data than with use of the uncensored data. The reason for the differences was because there were 11-day egg-laying sequences in which few or no eggs were produced or that egg laying was declining in midlife. These are the patterns for the independent variables that were associated with and thus predictive of the terminal segments. These midlife patterns occurred more in in the two tephritid species than in D. melanogaster and thus helps explain the higher performance levels for the regression model in this species. Third, the performance metrics for D. melanogaster were extraordinarily high relative to those for the two tephritids as well as absolute.

These weak correlations, combined with the rarefaction curve, mean that we needed to rarefy samples to make proper comparisons without jeopardizing the accuracy of sample diversity or losing information on OTUs, particularly rarer ones, in the data set. Hence, we chose the depth of 30,000 based on the rarefaction curve so that the number of OTUs in most samples could be accurately represented.After rarefaction, we compared the number of OTUs to that before rarefaction. Just like the preliminary experiments, rarefying to an even depth reduced the absolute numbers of OTUs across all samples while retaining the relative trends . The similarity between the OTU distributions before and those after rarefaction speaks to the efficacy of this technique in terms of standardizing sample sizes while avoiding loss of useful comparative information about the community structure. To look at diversity more critically from a different angle, we also examined the inverse Simpson’s index vales before and after rarefaction . In this case, rarefying to 30,000 led to somewhat more pronounced effects on this index than on the number of OTUs. For all samples except for pure E. coli, rarefaction shrank the range of the inverse Simpson’s index values, thus making samples look more similar to one another. For instance, blueberry plant pot the culture with a value of more than 12 before rarefaction , compared to the rest of the cultures with values less than 5, saw a reduction to approximately 4, compared to the rest of the cultures that had inverse Simpson’s values of 3 or lower.

Nevertheless, the relative diversity rankings across samples were retained, and the difference between that particularly diverse culture sample and the rest of the cultures was still pronounced enough after rarefaction that this statistical procedure remained valid for temporal cultures. Principal Coordinate Analysis of all samples with spike-ins shows distinct clusters of cultures, controls, and plaque . As in the preliminary experiments, rarefaction does not visibly change the clustering patterns except for a few cultures of longer incubation times . Bar plots of read counts for negative controls show that the Escherichia-Shigella OTU, the major OTU in the E. coli spike-ins, dominates the negative controls for Host 3 until the 168-hour incubation time. For Host 1 and 2 controls, this OTU is not consistently dominant as incubation time increases, and the lack of consistent dominance is shown clearly in bar plots of relative abundances of controls . In this figure, we see that Escherichia-Shigella OTU0001 occupies more than 90% of the read counts in Host 1 for only the 12-hour controls and in Host 2 for the 12-hour controls, one 24-hour control, and one 48-hour control. After 48 hours, the relative abundance of the Escherichia-Shigella OTU does not consistently decrease as incubation times increase, and the relative abundances of the spike-in OTU differ across the two wells from the same incubation time, especially for the 168-hour controls in all hosts. These results are somewhat difficult to interpret because of the uneven sequencing depths of the controls , as well as the experimental setup in which we sampled from distinct sections of the well plates instead of the same wells.

However, one observation is clear – the contamination is still entirely internal, i.e. sourced from the cultures from the same plate. Contaminated controls in all hosts are dominated by Streptococcus OTUs until Veillonella OTUs take over at 168 hours, and as we will observe later, this succession occurs in the cultures as well. Together, these results validate that our methodology prevents external contamination. The relative abundances of cultures and plaque, with spike-ins, are shown in Fig-ure 21. The plaque samples contain very little biomass, as expected while cultures from all hosts contained high biomass from oral bacteria, as evidenced by the dominance of non-Escherichia-Shigella OTUs. Dominance of the oral bacteria, however, is not always consistent between the 2 wells with the same incubation time. This is especially visible in the 48- and 96-hour cultures from Hosts 2 and 3, where Wells 1 and 2 differ in terms of relative abundances of oral bacterial OTUs. Oral bacterial dominance also does not always increase with increasing incubation time. In fact, there seemed to have been a decrease in oral bacteria biomass, relative to the E. coli spike-in, from 48 hours to 96 hours for Hosts 3. Once again, these results are difficult to interpret because the wells being sampled at these time intervals were different wells. The inconsistency in the relative abundances of the oral bacterial OTUs could have easily arisen from differential growth rates of different wells. Such a possibility seems especially likely in light of the substantial differences in the read depths of the cultures . However, a definitively consistent trend did surface from this relative abundance data: Cultures from all 3 hosts show a predominance of Streptococcus OTUs for both the 12- and 24-hour cultures, followed by the rise of Veillonella OTUs , which is in turn followed by a shift toward Prevotella OTUs .

Host 1 relative abundances show the earliest visible appearance of the Prevotella OTUs at the 96-hour mark while Hosts 2 and 3 only show the presence of Prevotella OTUs in the 168-hour cultures. Such changes in these community compositions indicate a shared succession of colonization, much like the sequential colonization of the human oral cavity in vivo, where members from the Streptococcus genus lay the ground work, and those of the Veillonella and Prevotella genera follow as either middle or late colonizers. It is also interesting to note that regardless of initial plaque composition, even without continuous inoculation of the in vitro cultures by plaque, the communities in these experiments evolved to include OTUs from the Streptococcus, Veillonella, and then the Prevotella taxa. It is possible, then, that members of the Veillonella and Prevotella genera stay dormant and/or protected until they can proliferate in the environment created by Streptococcus OTUs, though this succession would be best tested by co-culturing known strains. We can observe an interesting similarity between the relative abundances of negative controls and those of the cultures/plaque samples. The community composition changes in the contaminated controls track the changes in the cultures, albeit at a slower rate; controls in these experiments did not reach a mature enough stage to include the Prevotella OTUs. It is likely that because the controls received the inoculum later during the incubation period – at some point between 12 and 24 hours rather than at hour 0 – their development seemed delayed compared to the cultures. After we examined the biomass of the controls and cultures, using the E. coli spike-in as a qualitative standard, we removed the major spike-in OTUs in order to assess sample similarities. We performed PCoA on the cultures and plaque samples without spike-in OTUs, and the results clearly show three clusters of samples regardless of rarefaction status . Plaque samples cluster loosely in a group separate from the cultures , as expected because of the inherently selective nature of in vitro culturing procedures. The large spread in the plaque samples is also expected because these samples originated from different hosts, though this cluster did not show three finer subclusters. Unlike the plaque samples, cultures cluster into two distinct groups, separated by the length of incubation time. The 12- and 24-hour cultures cluster somewhat tightly together while the 96- and 168-hour cultures cluster tightly together , with some spread of the 48-hour cultures in between . The two clusters are also situated somewhat symmetrically across the loosely vertical line formed by the plaque samples. The divide between cultures of different incubation times suggests a compositional shift in the cultures for all three hosts starting around 48 hours, as we observed in the relative abundances of the cultures, albeit with less certainty. Another feature of some interest in the PCoA plot is the amount of variation accounted for by the two coordinates. In this case, the differences in the cultures account for 55 to 57% of the total variation in the samples, plastic gardening pots while differences between cultures and plaque account for approximately 22 – 23% of the variation. The difference between Streptococcus dominance and Veillonella/Prevotella dominance is clearly the largest contributing to inter-sample variation. However, the two coordinates in PCoA only account for about 80% of the total variation, which puts into question what constitutes the other 20%.

To help answer this questions, we constructed a dot plot of OTUs that make up at least 1% of the relative abundance in rarefied samples , and performed Principal Component Analysis on the relative abundances . From the dot plot, we found that the most abundant OTUs in plaque samples came from 7 genus-level taxa , two of which include members known for early and middle colonization of the plaque community and one of which contains members that have been shown to co-aggregate with organisms involved in all stages of colonization. In cultures, we observe the trends present in the relative abundance bar plots and the PCoA plots: compositions of 12- and 24-hour cultures are dominated by Streptococcus OTUs , then transition to Veillonella OTUs in 48-, 96-, and 168-hour cultures, with the simultaneous rise of Prevotella OTUs at 96 and 168 hours. Of the Streptococcus and Veillonella OTUs, a single OTU from each genus dominates at certain points in time while the other OTUs tend to persist at lower abundances. On the other hand, only one Prevotella OTU plays a role in the temporal cultures. Interestingly, neither this Prevotella OTU nor a Fusobacterium OTU appears much in the cultures until 168 hours of incubation. In addition, a Megasphaera OTU also begins appearing between 48 and 96 hours of incubation. Other OTUs with somewhat substantial presence in plaque samples Pseudomonas OTU0027, Corynebacterium OTU0017, Actinobacillus OTU0019, and Acinetobacter OTU0030 – seemed to have been selected out of the temporal cultures, as they do not appear in abundances higher than 1%, and all except for the Actinobacillus OTU disappears between 96 and 168 hours of incubation. Based previous research on the order of succession in human oral microbiome formation, we see that these temporalcultures were potentially transitioning into later or late colonization stages at 168 hours, with the rise in relative abundance of the Fusobacterium OTU. Here, extending the time incubated beyond 168 hours and/or regular re-inoculation with host-specific plaque would help us greatly in probing whether such a transition is occurring in vitro. For the Principal Component Analysis , we first performed it on untransformed relative abundances of plaque and culture samples. The resultss show distinct clusters much like in PCoA , with plaque samples situated between cultures of different incubation times. The underlying factors that contribute toward differential clustering seem to originate from a division between Streptococcus OTU0002 and Veillonella OTU0003. As expected based on results in the dot plot, Pseudomonas OTU0027 contributes to the difference between plaque samples and culture samples , though the other prominent plaque OTUs from the Corynebacterium, Actinobacillus, and Acinetobacter genera surprisingly do not seem to contribute as much as the Pseudomonas OTU does. When colored by host, the cultures display no visibly distinct grouping, though duplicate plaque samples of individual hosts stay quite close to each other . We then performed centered-log-ratio transformation on the relative abundances of plaque and culture samples, and performed PCA again on the transformed data. CLR is commonly used to take the simplex space of the relative abundance data of the sample – the nature of any data that sum to a value of 1, or 100, for any individual sample – into real Euclidean space, hence making valid any distance metrics and statistical method that can be applied to data in Euclidean space . Because PCA typically needs to operate in a real Euclidean space to avoid artifacts and spurious patterns, CLR allows us to perform PCA on the data set in a much more statistically valid manner. Mathematically, this transformation is done by using the log function on a ratio between a sample and the geometric mean of the sample, as shown in Eq. 4 . Results of PCA on the CLR-transformed relative abundances show that plaque and cultures cluster in groups similar to those in PCoA and PCA of untransformed relative abundances . As shown in the analyses above, the differences across hosts do not play a large role in accounting for the variation in the samples, but incubation time does.

The Nanopore and PacBio methods also tend to have high error rates, the lower limit being approximately 4.9% for insertion errors on the Nanopore platforms, and the higher limit being 11% for insertion and/or deletion on the PacBio platforms. The third platform, the Ion Torrent, outputs reads of 200bp and 400bp on two different machines at a relatively fast rate, but are prone to indel errors and homopolymers longer than 6bp. The length and historically high error rates of these three platforms make them unsuitable for the study of microbiome compositions by short genetic markers, though recent literature has shown that an optimized bioinformatics pipeline on the improved PacBio platform can achieve less than 0.01% error rate for full-length 16S rRNA sequences, though the read lengths of the PacBio platform remain suboptimal for experiments involving comparisons of large microbial communities. The fourth HTS platform comes from Illumina and has been indispensable for the study of microbiomes because of its high sequencing depth, speed of operation, cost efficiency, and low error rates. Illumina technology is based on clonal amplification on a glass surface and detection by cyclic reversible termination. Bases are detected by a coupled-charge device camera, square planter pots and the fluorescent signal incorporated into the incoming base can be easily cleaved after imaging. The most common error on Illumina platforms is substitution, and the error rate was already reduced to less than 1% morethan 10 years ago.

The MiSeq platform has been shown to be particularly suitable for studies of microbiome composition because it has low costs and short run times while still taking full advantage of the species-level differences in the variable regions of bacterial 16S rRNA component of the ribosomal 30S subunit. The species-level specificity of the conserved 16S rRNA region enables the classi- fication of organisms into operational taxonomic units , which serve as effective proxies for taxonomic levels. Great efforts have been made in determining the variable regions most suitable for distinguishing among microorganisms, and in creating the most efficient universal primers, for sequencing bacterial 16S rRNA . As universal primers and various error-correction software packages have been developed and refined, research on the human microbiome has experienced exponential growth. Previously undetectable, undifferentiable, and uncultivatable strains have been identified and classified, now aggregated into the particularly notable effort of the Human Microbiome Project, which has culminated in extensive records and repositories of the memberships, distributions, and interactions of human microbiota, including those of the oral microbiota. The exponentially growing research on the human microbiome has thrown into doubt onto the idea that a single organism or factor is responsible for the manifestation of a disease. The ability to compare community compositions between healthy and diseased hosts marked the beginning of a deepening understanding of human wellness, where the concept has emerged that some diseases may originate from the imbalance of microbial communities rather than from the actions of single organism or a group of organisms.

This idea has taken hold in therapeutic approaches, most notably in faecal transplantations to treat obesity and some forms of inflammatory bowel diseases, though treatment of some other diseases such as Crohn’s Disease by fecal transplantation resulted in mild side effects. However, to date, no oral microbiome transplantation has yet been attempted, despite the ample evidence linking disrupted oralmicrobiota to many systemic diseases. There seems to be some efforts in this direction, shown by an exceedingly recent publication detailing a protocol for developing and characterizing an oral microbiome transplant, but no results as of December 2021. The length of time between the initial studies of the oral microbiome and the potential application of oral microbiome transplantations, or between the efforts toward fecal microbiome transplantations and the current efforts toward oral microbiome transplantations, could be explained by a need for pilot studies or for observations of long-term effects. An in vitro model of the dental plaque microbiome and/or the oral microbiome that can be readily generated, easily modified, and rapidly tested would contribute significantly to the research efforts on the therapeutic potential of the oral microbiome transplantation approach.As HTS using 16S rRNA markers rose prominently as the dominant approach for identifying microorganisms and characterizing bacterial community compositions, so did the need for assessment, quality control, and optimization of the sequencing process. From the first step of the sequencing process – the amplification step – there is a need for effective assessment of the quality of universal primers. Designing universal primers that are simultaneously specific for a variable region of the bacterial 16S rRNA and are able to capture different bacterial species and strains is no small feat – it’s been shown that even a single mismatch between the primer and template can lead to thousand-fold misrepresentation of the abundance of the sequence.

These differences in primer efficiency and specificity can result in distortions in the apparent relative abundances of a community, and much effort has been devoted to optimizing primer pairs that capture the widest range of organisms and exhibit the highest specificity and reproducibility. Errors in other steps of the HTS process, such as PCR amplification and incomplete reactions during sequencing also affect the quality of the sequencing data. Raw sequences, therefore, need to be checked and filtered, and various tools have been constructed for these purposes. Some tools are built into software packages such as mothur, QIIME, and DADA2; others were created as standalone insertables that can be introduced into workflows. In addition to checking the quality of the raw reads, investigators have adopted internal sequencing standards as part of routine procedure as quality control for each sequencing run. After preprocessing raw reads to retain only high quality reads, identifying and classifying members of a community come next. A number of methods have been developed to this end, most of which fall into either phylotyping or OTU clustering. Phylotyping assigns reads into bins based on read homology and reference sequences such as those in the Human Oral Microbiome Database; OTU clustering, which frequently uses the naïve Bayesian classifier, assigns reads into bins based on distances between reads, with percent similarity cutoffs. These two approaches can be used to complement each other, as phylotyping has difficulty treating unknown organisms or incomplete sequences in the databases and OTU clustering can exhibit ambiguities that result from ill-defined percent similarities, especially when sequencing errors lead to spurious OTUs and overestimated diversity. The identification of organisms in allows for comparisons of different organisms within a sample and between samples. Members and their abundances within a sample are collectively known as the “alpha diversity” of that sample, whereas the differences between memberships and abundances across two or more samples are collectively known as “beta diversity” . Different indices have been developed to quantify these two types of diversities. Currently, there exist, for both types of diversity, indices that only account for the absence and presence of members as well as indices that account for membership and their distribution . A number of alpha diversity indices have been particularly popular in microbiome studies, including Simpson’s index, which represents the probability that two individuals randomly drawn from a sample belong to the same type; and Shannon index, which quantifies the probability of predicting the identity of the next individual drawn from a sample, based on the sample size and relative abundances. Simpson’s index is frequently used in its reciprocal form, called Inverse Simpson’s index, which represents the effective number of types. Inverse Simpson’s index is more influenced by dominant OTUs while Shannon index is more influenced by rare OTUs, so they are often used in conjunction to examine different aspects of diversity in a community. Interestingly, square pot beta-diversity indices seem to be infrequently used in microbiome research. Instead, the community has taken to using variance and distance measures in the characterization of core microbiome across different body sites and different hosts and the comparison of microbiome compositions in healthy and diseased states. A common way to assess beta diversity in microbiome research is using inter-sample distance measurements. In these types of analyses, abundance data is sorted into matrices with samples as rows and species or OTUs as columns. Distance measures representing dissimilarities between pairs of samples are computed, and the resulting triangular matrix is used for ordination approaches such as Principal Coordinate Analysis and/or Principal Component Analysis.

A number of different distance measurements have been adopted for these ordination approaches. Distance measurements, like alpha diversity indices, include those that consider membership only, those that consider membership and abundance, and those that consider phylogenetic relatedness, though the distance measurements that account for membership only are not as commonly used. Of the most frequently used indices, Bray-Curtis and weighted UniFrac, account for the abundance as well as the presence and absence of taxa, and UniFrac also considers phylogenetic relatedness. As for the implementation of these indices in ordination techniques, PCoA uses distance matrices to construct clusters of similar samples, and PCA uses distance matrices as well as ma-trix transformations to visualize sample similarities. Both techniques reduce the dimensionality represented by the large number of OTUs in microbiome datasets. In many cases, PCA and PCoA reduce the dimensionality to two or three dimensions for ease of visualization and elucidation of the major factors underlying inter-group and inter-sample similarities. The purpose of beta-diversity assessment and ordination is most often to delineate the relationships among samples, hosts, or other meta-data groupings. Of course, more rigorous statistical testing can be performed with microbiome datasets. Currently popular approaches stem from multivariate statistics, as conventional statistics are based on count data and absolute values in Euclidean space instead of relative abundance data in simplex space where abundances sum to 1 or some other constant. In simplex spaces, conventional statistical procedures such as the t-test and ANOVA can lead to high false discovery rates, in many cases because of the assumption that the underlying population distribution adopts a predefined shape . Efforts to circumvent the distributional assumption problems have led to approaches such as ANOSIM – analysis of similarities, for which no underlying population distribution is assumed but the null hypothesis of “no differences exist among samples or groupings” can still be tested; ANCOM – analysis of composition of microbiomes – an approach that also does not rely on distributions and can be implemented in linear model frameworks; and PERMANOVA, an approach based on analysis of variance that is independent of underlying distributions as well as metric distances but can still partition variances based on any distance measure. As will be evident later, the data from this project is best analyzed with PCoA, PCA, and some limited use of ANOSIM.Over the decades, there have been efforts to construct in vitro models of the human oral microbiome. Much of this effort has focused on generating laboratory conditions that most closely match those in in vivo environments. Like microorganisms in other environmental niches, human oral microorganisms form biofilms to increase their physical proximity to one another, allowing for inter-strain and inter-species cooperation for biomass accumulation and against environmental fluctuations. Hence, most oral microbiome models have been designed to promote biofilm formation. These models vary in device shape, substrate type, media composition, incubation time, and species in the inoculum. Some models pre-condition culture plates with artificial pellicle, paying little heed to the exact characteristics of the substrate surface ; others, in addition to the artificial pellicle, use substrates with surface properties similar to those of oral surfaces. Some models adopt continuous flow devices or rotating devices to mimic the salivary sheer forces in the host oral cavity; others forego this aspect of the oral cavity. Some models use host communities to inoculate the cultures; others use a number of laboratory strains to form the inoculum. Most models use media components intended for fastidious organisms – brain heart infusion, media constituted from various components that supply different amino acids, pig mucin as a major carbon source – and receive supplements such as vitamins and siderophores. Some models have complex designs that try to mimic the oral cavity while permitting non-invasive sampling and regular or continuous measurement; others seek the minimal equipment necessary for generating a model community. Despite the ample number of different models, not many longitudinal ones have been developed; those that have incubation periods of longer than 48 hours tend to use devices that supply a near-constant stream of nutrients and saliva, and little research has been done to generate a flow-device-free, fermenter-free model. Because of the lack of research in this area, we aimed to devote a considerable portion of our project to temporally extending the 24-well cultivation method with the longitudinal component to maximally simplify the procedures while retaining reproducibility.

China will likely be one of the first countries in the world to commercialize GM rice. In the United States, the two most widely visible, potentially commercially viable transgenic rice cultivars are Roundup Ready® rice by Monsanto and LibertyLink® by Bayer CropScience . Both are HT varieties—the former is resistant to Roundup® and the latter to Liberty® , both nonselective herbicides able to control a broad spectrum of weeds . Glyphosate is currently registered for rice in California but not widely utilized while glufosinate is not registered [California Department of Pesticide Regulation ]. As such, it is unlikely that local weeds have developed a natural resistance to these chemicals, unlike, for example, bensulfuron methyl . In 1999, LibertyLink® rice cleared biosafety tests by USDA’s Animal and Plant Health Inspection Service but is not commercially available at this time . The primary direct effects of HT transgenic-rice adoption on the cost structure of California rice growers are reductions in herbicide material and application costs and the likely increased cost of transgenic seed. An HT cultivar differs from conventional seed in that a particular gene has been inserted into the rice plant that renders the species relatively unharmed by a particular active chemical ingredient, thus allowing application of broad-spectrum herbicides directly to the entire planting area . This has the potential to simplify overall weed management strategies and to decrease both the number of active ingredients applied to a particular acreage and the number of applications of any one herbicide, blueberry container thus decreasing weed-management costs.

Reduced chemical use provides the major cost saving for growers. Similarly, herbicide application costs per acre depend on the specific chemical involved and the means of application. Typically, application by ground is 60 to 80 percent more expensive than aerial applications . For this study, other pest-management practices and fertilizer applications are assumed not to change with adoption of HT rice. The cost of transgenic rice seed will be greater than that of conventional seed because companies that sell transgenic varieties typically charge a premium to recoup their research investment costs.8 Based on Roundup Ready® corn and soybeans as a reference point, the technology fee is approximately 30 to 60 percent of conventional seed costs per acre . Seed price premiums are in a similar range for Bt corn varieties . In addition to the technology fee, seed costs for transgenic rice will likely change as a result of the California Rice Certification Act of 2000 signed by Governor Gray Davis in September 2000. With the full support of CRC, the CRCA provides the framework for a voluntary certification program run by the industry, offering assurances of varietal purity, area of origin, and certification of non-GM rice . A second, mandatory provision of the CRCA involves classification of rice varieties that have “characteristics of commercial impact,” defined as “characteristics that may adversely affect the marketability of rice in the event of commingling with other rice and may include, but are not limited to, those characteristics that cannot be visually identified without the aid of specialized equipment or testing, those characteristics that create a significant economic impact in their removal from commingled rice, and those characteristics whose removal from commingled rice is infeasible” . Under this legislation, any person selling seed deemed to have characteristics of commercial impact, which would include anytransgenic cultivars, must pay an assessment “not to exceed five dollars per hundredweight.”

This fee is currently assessed at $0.33 per cwt with specific conditions for planting and handling divided into two tiers .10 In addition, the first handler of rice having these characteristics will pay an assessment of $0.10 per cwt . The $0.33 seed assessment is approximately 2.4 percent of average seed costs while the $0.10 fee represents 1.5 percent of average output price. A portion of these assessments is likely to be passed to the grower, depending on the relative elasticities of supply and demand in the seed and milling markets. In addition to generating cost savings, cultivation of HT rice will affect revenues as well. Net returns will be positively correlated with transgenic yield improvements. HT crops are not engineered to increase yields; rather, they are designed to prevent yield losses arising from pest or weed infestation. As such, potential yield gains depend on the degree of the pest and/or weed problem and the efficacy of the HT treatment relative to the alternatives. Many adopters of transgenic corn, cotton, canola, and soybeans have experienced positive yield effects on the order of 0 to 20 percent . However, under more ideal conditions, a yield drag may occur if the cultivar exhibiting the genetic trait is not the highest-yielding variety or if the gene or gene-insertion process affects potential yields . Field tests of LibertyLink® in California have generally found a yield drag of between 5 and 10 percent relative to traditional medium-grain M-202 varieties . To the extent that a yield drag actually exists in the field, it is expected to quickly dissipate over time as a greater number of varieties with the HT trait become available.Another effect of GM rice cultivation on California growers’ returns is the potential development of price premia for conventional medium-grain rice varieties in world rice markets. Despite the predictions and evidence of producer financial benefits from transgenic crops, there is demand uncertainty in world grain markets, especially in the European Union and Japan . Although challenged by many of the major transgenic-crop producing countries , the EU has prohibited imports of new GM crops.

Many other countries have varying GMcrop threshold labeling regulations, including China, Japan, the Republic of Korea, the Russian Federation, and Thailand . These regulations have the potential to ensure that there is some demand for non-GM grain. Due to segregation requirements and the higher unit cost of production of non-GM crops, this introduces the potential for a price premium for non-GM rice. As a result, nonadopters may indirectly benefit from the introduction of transgenic rice. There is good evidence that foreign regulations have affected export demand for transgenic crops, but there is mixed evidence of price premia for traditional non-GM grains. For example, after the United States started growing GM corn, EU corn imports from the United States dropped from 2.1 million metric tons in 1995 to just under 22,000 metric tons by 2002 [USDA, Foreign Agricultural Service 2003b]. Notably, however, the gap in U.S. corn sales to the EU was filled by Argentina, a transgenic producer that only grows varieties approved by the EU . On the other hand, imports of U.S. corn byproducts to the EU have dropped only slightly since 1995 . The U.S. GM soybean export share in Europe has suffered as well, declining by more than 50 percent since 1997 . Price premia exist for non-U.S. corn in Japan and the Republic of Korea, traditional soybeans in Japan, and non-transgenic corn at elevators in the U.S., typically ranging from 3 to 8 percent . However, there is little evidence for price differentials between the GM and non-GM product in the canola market . The global market for rice differs from the market for soybeans in that the majority of rice sold is for human consumption rather than for animal feed. As a result, the market-acceptance issue is likely to be a key determinant of the success of transgenic rice adoption in California . As can be seen in Table 1, the export market for California rice accounts for approximately one-third to one-half of total annual production with Japan and Turkey as the major destinations. California Japonica rice imported by Japan is channeled through a quota system that was negotiated at the Uruguay Round in 1995. Most of California’s rice exports are purchased by the Japanese government and used for food aid and for other industrial uses, including food and beverage processing . Only a small portion of this imported high-quality rice is released into the domestic Japanese market .

Turkey is reportedly attempting to severely restrict imports of transgenic crops through health regulations, despite importing corn and soybeans from the United States , growing blueberries in containers while Japan requires labeling of 44 crop products that contain more than 5 percent transgenic material as one of the top three ingredients . Currently, several varieties of HT and viral resistant rice have entered the Japanese regulatory system for testing but have not yet been approved for food or feed use . As an illustration of potential market resistance, Monsanto suffered setbacks in Japan in December 2002 when local prefecture authorities withdrew from a collaborative study to develop a transgenicrice cultivar after being presented with a petition from 580,000 Japanese citizens . In 2002, China imposed additional restrictions on transgenic crops, including safety tests and import labeling . However, this action may be nothing more than a trade barrier to reduce soybean imports from the United States. In addition, China is worried that introducing biotech food crops may jeopardize trade with the EU. Nevertheless, China is not taking a back seat in transgenic crop research, as it has a major ongoing research program on biotech rice and other crops and is predicted to be an early adopter . There is also some skepticism in the United States with regard to GM crops. Aventis was sued in 2000 over accidental contamination of taco shells by transgenic corn that was not approved for human consumption, resulting in an expensive food recall. The company subsequently decided to destroy its 2001 LibertyLink® rice crop rather than risk its potential export to hostilenations . Kellogg Company and Coors Brewing Company have publicly stated that they have no plans to use transgenic rice in their products due to fears of consumer rejection, and several consumer and environmental groups favor labeling of foods made from transgenic crops . For most food and beverage products manufactured by these companies, however, rice accounts for a small input cost share, resulting in little financial incentive to support GM crop technology. In May 2004, Monsanto announced that it was pulling out of GM wheat research in North America, partly due to consumer resistance. This has important implications for commercialization of GM rice because both grains are predominantly food crops. Many California rice farmers are concerned over the confusion regarding GM crops and do not want to jeopardize export market sales. This fear has been exacerbated by Measure D on the November 2004 ballot in a major rice-producing county that would have prohibited farmers from growing GM crops. A 2001 survey of California growers performed by the University of California Cooperative Extension showed that, of the respondents, 24 percent planned to use transgenic varieties, 37 percent would not, and the remainder were undecided . Of those growers who answered “no,” 78 percent responded that market concerns were a reason. Nevertheless, if profitability at the farm level increases, it is likely that a subset of California producers will adopt the technology . Presumably, those with the most significant weed problems and hence the highest costs would be the first to adopt.UCCE produces detailed cost and return studies for a wide variety of crops produced in California, including “Rice Only” and “Rice in Rotation.” The studies are specific to the Sacramento Valley region where virtually all California rice is produced. Figures on herbicide applications are based on actual use data as reported by DPR and UC Integrated Pest Management Guidelines . The most recent study completed for rice is by Williams et al. and is used as the basis for this study. As the potential adoption of transgenic rice is unlikely to significantly change farm overhead expenses on average, we focus on returns and operating costs per acre as reported in the sample-costs document. However, given weed-resistance evolution, changing regulations from DPR, and changes in the 2002 Farm Bill, the baseline cost scenario is adjusted here to account for changes in herbicide-use patterns, prices of herbicides and rice, and projected government payments. Using information from the 1999 pesticide use report compiled by DPR, the 2001 sample costs assume applications of bensulfuron and triclopyr, both broadleaf herbicides, on 25 and 30 percent of the acreage, respectively, and applications of the grass herbicides molinate and methyl parathion on 75 and 45 percent, respectively, of the acreage. These figures are updated using data from Rice Pesticide Use and Surface Water Monitoring, a 2002 report by DPR, as interpreted by the authors.