Philippa Kaur

The JJAS river heights at Hay (Fig. 2a) have clearly reduced variability over the 27-year period 1965–1991 compared with 1992–2018 (p-value = 0.005, Table 4). Despite the apparent decrease in the mean and variance of JJAS Murrumbidgee River heights at Hay from the 1960s, as shown by the low p-values (0.309, 0.332), respectively, for the 27-year intervals 1938–1964 to 1965–1991 (Table 4), the decrease is not statistically significant. However, the decrease is consistent with the suggestion that a change point occurred from the late 1950s between unregulated to regulated flow at Hay11.While the mean JJAS precipitation of the three catchment locations of Burrinjuck Dam, Blowering Dam and Tumut indicate a slight decrease in percentile extremes from the 1990s, with 2016 (due to September) only above the 95th percentile (Fig. 3), they exhibit no significant change in mean or variance based on the bootstrapped intervals JJAS 1964–1991 to 1992–2018. Consequently, the question arises of why the significant decrease in mean and variance of the JJAS Murrumbidgee River height at Wagga Wagga and in variance at Hay does not match a similar significant decrease in mean or variance of JJAS catchment rainfall. A rainfall decline in recent decades was found to be most pronounced in late autumn20,21 and that without sufficient autumn rainfall to moisten catchments in southern Australia, follow-up rainfall in winter cannot be efficiently converted to run-off and catchment inflows22. There have been statistically significant decreases in April–May mean precipitation at the catchment locations of Blowering Dam, Burrinjuck Dam and Tumut from 1964 to 1991 to 1992–2018 and also for the mean inflows to the two Dams (Table 4). Furthermore, as a result of the Millennium Drought (1997–2009), modelling experiments indicate that, starting from very dry conditions, the run-off response to rainfall only will return to the normal pre-drought conditions after about 10–20 years of average rainfall23. Therefore, the significant decrease in variance of Murrumbidgee River heights at Hay and in mean and variance at Wagga Wagga, is most likely due to the April–May reduced dam inflows and precipitation, and from average JJAS catchment precipitation since 1991. Any role played by water extraction for irrigation between Wagga Wagga and Hay, where irrigation is concentrated, is likely to be small owing to the highly significant mean river height reduction at Wagga Wagga which is upstream from Hay. However, irrigation, and other water usage, is sourced from the dams, so there is a long-term impact of irrigation over the months preceding JJAS on flows at Wagga Wagga, due to the reduction in water stored in the upstream dams. The dams integrate the water extracted for irrigation and all other usage since the last spill event, and therefore the extractions over an extended period can have an impact on when the dam will fill, and hence on the flows downstream, including Wagga Wagga. The minimum water level in the dams, which typically occurs near the end of Autumn, is due to the reduction in inflows (impacted by climate change), and extractions from the dam. Coupled with the tendency for a slower fill rate due to reduced inflows, this results in fewer spill events. As a consequence, there is a change in the distribution between spill events and irrigation releases, changing the frequency distribution of flows. This will be particularly the case for the JJAS period, as a delayed dam filling will have a major impact in dam levels in that period. Before the 1990s the river at Wagga Wagga and Hay reached flood level height or close to flood level height regularly in JJAS from precipitation-driven inflows regardless of the amount of water that was extracted (see Fig. 2a,b). Since the mid-1990s less water reaching Wagga Wagga has significantly reduced the river height owing to significantly decreased April–May and JJAS precipitation-driven inflows at the upstream catchment dams of Burrinjuck and Blowering Dams and significantly decreased mean precipitation at Tumut, which also represents the catchment area of Blowering Dam (Fig. 2c,d; Table 4). Moreover, there has been no overallocation or hoarding of water found in the southern MDB24. In a different southern MDB catchment study of the Millennium Drought 1997–2008, factors for a disproportionate reduction in rainfall run-off were reduced mean annual rainfall, less interannual variability of rainfall, changed seasonality of rainfall and lastly increased potential evaporation25. However, the last two factors mentioned have since become well established in the last decade with reference to the work in this study. It was suggested that a rainfall reduction alone does not explain the observed inflow reduction trend26. Even after a major rain event, the soils are so dry that they absorb more water than before the rain event, and less reaches the dams and rivers than on a wet catchment. In the last three decades it is unknown what the effect on run-off into dams and the Murrumbidgee river has been in JJAS from major rain events because, apart from August–September 2016, there have been no major catchment net inflows since 1991 (Fig. 2c,d). There were significant precipitation-driven inflows during SON 2010 which led to flood level exceedances at Wagga Wagga and Hay in December 2010. In June and July 1991 there was a series of rain-producing cut-off low pressure systems over inland NSW and the adjacent coast influencing the catchment, interspersed with persistent, precipitation-producing frontal systems embedded in the westerly airflow during July and August. Rain producing inland cut-off low pressure systems over southeast Australia are the main influence on enhancing JJAS rainfall totals8.Decreased JJAS precipitation in continental southeast Australia has been evident for at least the last two decades, as anticipated by climate scientists. The naturally periodic La Niña phenomenon provided spring and summer precipitation during much of 2010 to 2012, which ended the Millennium Drought (1997–2009). The only other recent widespread significant rainfall in southeast continental Australia was in August–September 2016 due to a negative phase of the Indian Ocean Dipole (IOD). A negative IOD phase typically is associated with wetter than normal spring conditions for southeast Australia7,8.Although the SAM is an atmospheric index with a time scale typically of a few weeks, an annual average SAM reconstruction shows that since the 1970s it is in its most positive state over at least the past 1000 years27. Prior to the 1990s soil wetness would have been in phase with the annual cycle of winter/spring peak rainfall, dry summer/early autumn and without a long term trend in SAM. However, because SAM has trended positive since the 1970s, the annual cycle of soil wetness of the MDB has been increasingly disrupted particularly since the Millennium Drought23 and there is also a potential long-term impact from groundwater systems28. This is supported by the most recent available annual area-averaged actual evapotranspiration and soil moisture deciles in Fig. 7a,b. These figures show the anomalously dry MDB catchment area in the period 2018–2019. In the southeast corner of the MDB, actual evapotranspiration is below average and soil moisture is very much below average.Figure 7Available at: http://www.bom.gov.au/water/nwa/2019/mdb/climateandwater/climateandwater.shtml.Annual deciles of actual evapotranspiration and soil moisture 2018–2019. Map of southeast Australia showing for the MDB region deciles during the 2018–2019 year for, (a) annual area-averaged actual evapotranspiration. Note the below average decile in the southeast corner of the MDB, and (b) annual area-averaged soil moisture. Note the very much below average decile in the southeast corner of the MDB. (Reproduced with permission under Creative Commons Attribution Licence 3.0 from the Australian Bureau of Meteorology.Full size imageTwo Supplementary Tables showing historical April–May (S1) and JJAS (S2) maximum river heights above flood level at Hay (6.7 m) in IPO phases indicate, as expected, more in negative IPO phases than positive phases and importantly a dissociation with the IPO resulting from none in the most recent negative phase from 1998 and the preceding positive phase after the early 1990s. The implication is that accelerated global warming since the 1990s has overwhelmed the influence of negative IPO on precipitation.The MDB plan, introduced from 201329 provided, for the first time, regulated allocations to environmental flows for ecosystem sustainability of rivers in southeast Australia such as the Murrumbidgee. However, the plan requires that each year on 1 July a fixed amount of water is locked in for future consumption, split three ways with the highest priority for human consumption and irrigation for permanent crops (e.g., fruit trees and nuts). The remaining allocations are split between non-permanent crops (e.g., cotton, rice) and environmental flow. A major issue is that the forecast net inflows upon which the allocations are based are the minimum inflows experienced in the 120 years up to the end of the twentieth century. However, as shown, even lower inflows have been experienced in the past two decades. It is not surprising that there is a significant decrease in the JJAS variance of the Murrumbidgee River height at Hay (p-value = 0.005) and both the JJAS mean and variance at Wagga Wagga from the periods 1965–1991 to 1992–2018 (mean p-value = 0.0044, variance p-value = 0.095; Table 4) since this period corresponds with the significantly reduced mean April–May catchment precipitation and mean April–May dam net inflows. The fact that there has been no significant change in the mean Murrumbidgee River height since 1991 is an indication that there has been a lack of major April-September rain events. The lack of significant catchment rainfall events from April to September is the reason for the reduction in the mean and variance of river heights at Wagga Wagga. Floods in April–May are rare along the Murrumbidgee River and the six years since 1874 in which April–May floods occurred at Hay prior to 1991 (Table 3), were dominated by precipitation that occurred as a result of mid-latitude interaction with either tropical or subtropical moisture, whereas the last flood that occurred in March 2012, was the result of a rain-producing tropical low pressure trough in the easterly wind regime that extended from northwest Australia to a low pressure centre in southern New South Wales near the Murrumbidgee catchment. Moreover, given the significant decline in April–May, Murrumbidgee catchment rainfall, JJAS run-off into the dams and Murrumbidgee River height at Wagga Wagga since 1991, the implication for water allocations of irrigated agriculture downstream from Wagga Wagga and for flood plain environmental flows required for sustainable wetlands downstream from Hay, will continue to be a problem. More

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Seed sourceSeeds of a segregating GR B. scoparia population identified from a wheat field (45°54′54.76″N; 108°14′44.15″W) in 2013 in Hill County, Montana, USA (designated as MT009) were used. The field was under a continuous no-till wheat-fallow rotation for  > 8 years and had a history of repeated glyphosate use (at least 3 applications per year) for weed control during the summer fallow phase prior to winter wheat planting. The permission of land owner was obtained prior to B. scoparia seed collection. All experimental research and field studies on plants, including the collection of plant material complied with the Montana State University guidelines and state/US legislation. Seeds of the field-collected population were used to generate GS and GR B. scoparia subpopulations through recurrent group selection procedure as described below.Development of GS and GR subpopulationsField collected seeds of MT009 population were sown on the surface of plastic trays (53 by 35 by 10 cm) filled with commercial potting soil (VERMISOIL, Vermicrop Organics, 4265 Duluth Avenue, Rocklin, CA, USA) in a greenhouse in the fall of 2013 at the Montana State University Southern Agricultural Research Center (MSU-SARC) near Huntley, MT, USA. Growth conditions in greenhouse were maintained at 25/22 ± 2 °C day/night temperatures and 16/8 h day/night photoperiods supplemented with metal halide lamps (450 μmol m-2 s-1). After emergence, approximately 200 uniform seedlings were individually transplanted in plastic pots (10-cm diam) containing the same potting mixture and grown for 6 weeks. A set of three clones (3 shoot cuttings) from each plant were then prepared and transplanted in plastic pots (10-cm diam) as described by Kumar and Jha22. At the 8- to 10-cm height, all cloned seedlings were separately treated with 435 (0.5×), 870 (1×), and 1740 (2×) g ae ha−1 of glyphosate (Roundup Powermax, Bayer Crop Science, Saint Louis, MO, USA) where 1× = field-use rate of glyphosate. All three glyphosate treatments included ammonium sulfate (2% w/v). Glyphosate applications were made using a cabinet spray chamber (Research Track Sprayer, De Vries Manufacturing, RR 1 Box 184, Hollandale, MN, USA) equipped with an even flat-fan nozzle tip (Teejet 8001EXR, Spraying System Co., Wheaton, IL, USA), calibrated to deliver 140 L ha−1 of spray solution at 276 kPa. Treated seedlings were returned to the greenhouse, watered as needed, and fertilized [Miracle-Gro water soluble fertilizer (24-8-16), Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH, USA] bi-weekly to maintain good plant growth. At 21 days after treatment, clones surviving the 2× rate of glyphosate were considered as ‘glyphosate-resistant (GR)’ and the clones that did not survive 1× rate of glyphosate were considered as ‘glyphosate-susceptible (GS)’. The parent B. scoparia plants corresponding to survived (resistant) or not-survived (susceptible) clones were transplanted separately in 20-L plastic pots (group of 3 to 4 plants pot−1) containing same potting soil for seed production. All 3- to 4 plants in each pot were collectively covered with a single pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flower initiation to restrict cross-pollination between GR and GS plants. At maturity, seeds from the respective GR and GS parent plants were collected and cleaned separately using an air column blower. The collected seeds from GR plants were subjected to three generations of recurrent group selection with the 2× rate of glyphosate in each generation. Seeds of GS plants were also subjected to recurrent group selection for three generations without glyphosate. Progenies of the GS plants were grown and sprayed with 1× rate of glyphosate to confirm the susceptibility to glyphosate in each generation23. This procedure allowed the development of relatively genetically homogenous GR and GS subpopulations from within a single B. scoparia population.Determination of EPSPS gene copy numberPreviously established protocols were adopted to estimate the relative EPSPS gene copy number in seedlings of GR and GS subpopulations through quantitative real-time polymerase chain reaction (qPCR)16,17,18. The ALS gene was used as reference since the relative ALS gene copy number and transcript abundance did not vary across B. scoparia samples17,18,29. Relative EPSPS:ALS gene copy number is a ratio of EPSPS to ALS PCR product fluorescence. Due to small differences in amplicon size, qPCR run conditions, and fluorescence detection, the values presented were estimates of relative gene copy number29.A total of 600 seedlings from the GR (450 seedlings) and GS (150 seedlings) B. scoparia subpopulations (developed by recurrent group selection) were grown in a greenhouse at MSU-SARC near Huntley, MT, USA in 2015 and 2016 to select enough plants for the field study each year. At 4-to 6-cm height, young leaf tissues (100 mg) from each seedling were sampled, frozen with liquid nitrogen and ground into powder using mortar and pestle. Genomic DNA were extracted from the tissue samples using the protocol from Qiagen Dneasy plant mini kit (Qiagen Inc., Valencia, CA, USA). Genomic DNA quantity and quality were determined using a Smartspec Plus spectrophotometer (Bio-Rad Company, CA, USA) and gel electrophoresis with 1% agarose, respectively. High quality genomic DNA (260/280 ratio of ≥ 1.8) were used to determine the relative EPSPS gene copy number. Two sets of primers to amplify the EPSPS and ALS genes, the final reaction volume and reagents used for each qPCR reaction, and the qPCR conditions used in this study were the same as previously described by Kumar and Jha22. Each qPCR reaction was performed on a Bio-Rad 96-well PCR plate in triplicates and fluorescence was detected using CFX Connect Real-Time PCR detection system. A negative control consisting of 250 nM of each forward and reverse primer, 1× Perfecta SYBR Green supermix, and deionized water with no DNA template was included. The EPSPS genomic copy number relative to ALS gene was estimated by ΔCT method (ΔCT = CT, ALS-CT, EPSPS)18,29. The relative increase in the EPSPS gene copy number was calculated as 2ΔCT.Survival and fecundity traits of GR and GS B. scoparia subpopulationsSeedlings (4- to 6-cm tall) of GR and GS B. scoparia subpopulations with known EPSPS gene copy numbers were transplanted into a fallow field in the summer of 2015 and 2016 at the MSU-SARC near Huntley, MT, USA. All transplanted B. scoparia seedlings were equally spaced at 1.5 m apart from each other and all plants were fertilized biweekly [2 to 3 g of MIRACLE-GRO water soluble fertilizer (24-8-16)] and irrigated as and when needed to avoid moisture stress. Experiments were conducted with a factorial arrangement of treatments (Factor A and Factor B) in a randomized complete block design, with 6 replications. Each transplanted B. scoparia seedling was an experimental unit. The factor A (4 levels) was comprised of B. scoparia plants with 1, 2–4, 5–6, and ≥ 8 EPSPS gene copy numbers, which were categorized as susceptible, low, moderate, and highly resistant plants, respectively based on their percent visible injury response to glyphosate. The factor B (ten levels) was comprised of increasing rates of glyphosate applied as single or sequential applications. Current labels of glyphosate allow a total of 3954 g ae ha−1 in split POST applications in GR sugar beet. As per the label, the maximum glyphosate rate of 2214 g ae ha−1 is allowed from crop emergence to 8-leaf stage of sugar beet and 1740 g ae ha−1 of glyphosate from 8-leaf stage to canopy closure or 30 days prior to sugar beet harvest. Hence, the tested total glyphosate rates were 0, 108, 217, 435, 870, 1265, 1740 [870 followed by ( +) 870], 2214 [1265 + 949], 3084 [1265 + 949 + 870], and 3954 [1265 + 949 + 870 + 870] g ae ha−1 along with ammonium sulfate (2% w/v). Sequential applications were made at 7- to 14-day intervals, with first application at 8- to 10-cm tall B. scoparia seedlings using a CO2-operated backpack sprayer fitted with a single AIXR 8001 flat-fan nozzle calibrated to deliver 94 L ha−1. Glyphosate rates and applications timings were selected to simulate the 2-leaf, 6-leaf, 8–10 leaf, and the canopy closure stage of GR sugar beet.Data collectionPercent visible control (relative to the non-treated) on a scale of 0 to 100 (0 means no control and 100 means complete plant death30) for each individual plant (240 plants total each year) were assessed at 7, 14, and 21 days after glyphosate treatment. Data on number of days from transplanting to 50% flowering (half of the inflorescences from each plant were covered with visible flowers) and seed set (seeds on half of the inflorescences from each plant were turned brown) were recorded for an individual plant. Each plant was covered with a pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flowering to prevent any cross-pollination. At the time of flowering, pollens from each survived plant were collected in early morning hours (between 8 to 10 am). At maturity, each individual plant was harvested and threshed to determine 1000-seed weight and seeds plant−1.Pollen and progeny seed viabilityPollens and seeds collected from individual B. scoparia plants (240 plants total each year) were tested for viability using a tetrazolium test. Pollens were collected in petri dishes by shaking the whole plant at the time of flowering. Four sub-samples of pollens from each petri dish were transferred into glass slides. The pollens in the glass slides were soaked with a tetrazolium chloride solution (10 g L−1), sealed with a cover slip using a nail polish and were incubated at room temperature for an hour. Viable (red) and non-viable pollens (yellow/white) were counted using a simple microscope. The physical structure of viable and non-viable pollens was also checked for any deformity using a compound microscope. Pollen viability for individual plants (240 plants total each year) was calculated as percent viable pollens of the total number of pollens counted.For seed viability test, twenty-five intact seeds collected from each individual plant (240 plants total each year) from the field were evenly placed in between two layers of filter papers (WHATMAN Grade 2, SigmaAldrich, St Louis, MO, USA) inside a 10-cm-diameter petri dish. Seeds were soaked with a 5-ml of distilled water and the filter papers were kept moist for the entire duration of the germination test. Light is not required for B. scoparia seed germination31, so the petri dishes were wrapped with a thin aluminum foil and placed inside an incubator (VMR International, Sheldon Manufacturing, Cornelius, OR, USA) with alternating day/night temperatures set to 20/25 °C23. Seeds with a visible uncoiled radicle tip longer than the seed diameter was considered germinated32,33. Radicle length was measured from three randomly selected germinated seeds 24 h after incubation to test the seedling vigor. The number of germinated seeds in each petri dish were counted daily until no further germination was observed for 10 consecutive days. Non-germinated seeds were tested for viability by soaking the seeds with tetrazolium chloride solution (10 g L−1) for 24 h23,34. Seeds with a red-stained embryo examined under a dissecting microscope (tenfold magnification) were considered viable35. Seed viability was expressed as the percentage of total viable seeds.Relative fitness (w)Fitness is the evolutionary potential for success of a genotype based on survival, competitive ability, and reproduction. Individuals with the greatest number of offspring and with the most genes contributing to the gene pool of a population are considered most fit genotypes36. Fitness of a genotype is determined by comparison of its vigor, productivity or competitiveness relative to the other genotype by quantifying specific traits such as seed dormancy, flowering date, seedling vigor, seed production, and other factors that can possibly influence the survival and reproductive success of a genotype36,37. In this study, relative fitness (w) of GR B. scoparia was calculated as the reproductive rate (seed production plant−1) of a resistant genotype (B. scoparia plants with 2–4, 5–6, and ≥ 8 EPSPS gene copies) relative to the maximum reproductive rate of the susceptible genotype (B. scoparia plants with 1 EPSPS gene copy) in the population. The relative fitness (w) of susceptible plants was assumed to be one.Statistical analysesA natural logarithm transformation was performed on data for time to 50% flowering, time to seed set, seeds plant−1. An arcsine square root transformation was performed on data for pollen viability, visible control, seed viability, and relative fitness (w) before subjecting to analysis of variance, however all data were presented in their back-transformed values. No transformation was needed for 1000-seed weight and radicle length data. Experimental year, B. scoparia plants with different EPSPS copy number groups, glyphosate rate, and their interactions were considered fixed effects and replication nested within a year was considered as a random effect in the model. Data on percent visible control, time to 50% flowering, pollen viability, time to seed set, 1000-seed weight, and seeds plant−1, seed viability and radicle length were subjected to ANOVA using Proc Mixed in SAS (SAS version 9.4, SAS Institute, Cary, NC, USA) to test the significance of experimental run, treatment factors, and interactions. The ANOVA assumptions for normality of residuals and homogeneity of variance were tested using Proc Univariate and PROC GLM in SAS. Means were separated using Tukey–Kramer’s HSD with α = 0.05. Furthermore, data on percent visible control and seeds plant-1 for each group of B. scoparia plants with different EPSPS gene copy number were regressed against total glyphosate rates using a four-parameter log-logistic model Eq. (1)38,39:$$Y=c+{d-c/{1+mathrm{exp}[bleft(mathrm{log}left(xright)-mathrm{log}left(ED50right)right)]}$$
(1)
where Y is the percent visible control or seed production plant−1 (% of nontreated); d is the upper asymptote (the highest estimated % control or % seed reduction); c is the lower asymptote (the lowest estimated % control or % seed reduction); ED50 is the effective rate of glyphosate needed to achieve 50% control or 50% reduction in seed production; and b denotes the slope around the inflection point “ED50.” Slope parameter (b) indicates the response rate of each group of B. scoparia plants with different EPSPS gene copy number to glyphosate rates (i.e., a slope with a large negative value suggests a rapid response of selected B. scoparia group). The Akaike Information Criterion (AIC) was used to select the nonlinear four-parameter model. A lack-of-fit test (P  > 0.10) was used to confirm that the nonlinear regression model Eq. (1) described the response data for each B. scoparia group38. Parameter estimates, ED90, and SR99 values (i.e. effective rate required for 90% control or effective rate required for 99% reduction in seed production) for each group of B. scoparia plants with different EPSPS gene copy number were determined using the ‘drc’ package in R software37,39. Parameter estimates of B. scoparia groups were compared using the approximate t-test with the ‘compParm’ and ‘EDcomp’ functions in the ‘drc’ package of the R software39,40. More

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