Mesophilic heterotrophic, aerobic or facultatively anaerobic bacteria that grow on yeast tryptone glucose extract agar were isolated from the surface of olive leaves of 3 or 4 different ages in January, April, July, and October from 1984 to 1989. Unweighted average linkage cluster analysis on either the Jaccard coefficient or the simple matching coefficient recovered 1,701 representative strains in 32 phena defined at the 70% and 80% similarity level, respectively. Of these, 25 were identified to genus or lower level. From the identity of the representative strains, the frequency of occurrence among the phylloplane bacteria over the 6-year period was estimated at 51% forPseudomonas syringae, followed byXanthomonas campestris (6.7%),Erwinia herbicola (6%),Acetobacter aceti (4.7%),Gluconobacter oxydans (4.3%),Pseudomonas fluorescens (3.9%),Bacillus megaterium (3.8%),Leuconostoc mesenteroides subsp.dextranicum (3.1%),Lactobacillus plantarum (2.8%),Curtobacterium plantarum (2.2%),Micrococcus luteus (2.2%),Arthrobacter globiformis (1.4%),Klebsiella planticola (1.2%),Streptococcus faecium (1.2%),Clavibacter sp. (0.98%),Micrococcus sp. (0.82%),Serratia marcescens (0.81%),Bacillus subtilis (0.57%),Cellulomonas flavigena (0.4%),Erwinia sp. (0.37%),Zymomonas mobilis (0.3%),Bacillus sp. (0.29%),Alcaligenes faecalis (0.27%),Erwinia carotovora (0.08%), andPseudomonas aeruginosa (0.04%). Bacterial communities on leaves of a given age at a given time during any one year displayed a very similar structure but differed significantly from those on the leaves of the same age at a different time or on the leaves of a different age at any time during any one year. Communities on the leaves of a given age at a given time of the year were invariably dominated by one or another of only 9 taxa, which accounted for 22 to 98.5% of the isolates from those leaves. The communities on 10- and 13-month-old leaves were invariably made up of fewer taxa than those on younger leaves at the same time of the year.

There were characteristic seasonal fluctuations in the populations of most of the bacteria. The abundance of P. savastanoi on healthy leaves in April and October supports earlier suggestions that the phylloplane of the host may be an important source of readily available inoculum in the epidemiology of olive knot disease.

No abstract

No abstract

Data assimilation has the potential to improve flood forecasting. However, research efforts are still needed for an effective development of assimilation schemes suitable for operational usage, especially in case of distributed hydrologic models. This work presents a new assimilation system of streamflow data from multiple locations in a distributed hydrologic model. The system adopts a mixed variational‐Monte Carlo approach, and is here tested with the hydrologic model MOBIDIC, that is part of the operational flood forecasting chain for Arno river in central Italy. The main objective of the work is to evaluate the actual gain that the system can lead to flood predictions in a real‐time operational usage. Accordingly, a specifically designed assessment strategy is employed. It is based on several hindcast experiments that include both high flow and false alarm events in the period 2009–2014 in Arno river basin. Results show that the assimilation system can significantly increase the accuracy of flow predictions in respect to open loop simulations in both cases. Specific performances depend on location and event, but in the majority of cases the error on predicted peak flow is reduced of more than 50% with a lead time of around 10 h. The analysis reveals also that the structure of the hydrologic model, the coherence between observations at various sites, and the initial watershed saturation level, considerably affect the obtainable performances. Conditions that may lead to a worsening of open loop predictions are identified and discussed.

One-hundred and twenty samples of lettuce and 89 samples of fennel purchased from five retail outlets in the city of Bari (Italy) from October 1973 through September 1975 were examined for viable aerobic bacteria (AB), total coliforms (TC), fecal coliforms (FC), fecal streptococci (FS), and salmonellae. Comparative tests indicated that the results of bacteriological analysis of wash water from either vegetable by a membrane filter technique compared favorably with those of conventional cultural examination of the vegetable tissue for the purpose of providing an indication of the bacteriological quality of the samples. Using the membrane filter technique, 2-year average counts of 6.59 x 107 for AB, 5.95 x 104 for TC, 6.13 x 103 for FC and 2.24 x 103 for FS/100 g (fresh weight) were obtained with lettuce; with fennel, the corresponding figures were 2.32 x 106 for AB, 7.82 x 104 for TC, 7.78 x 103 for FC, and 3.15 x 103 for FS. Indicator bacteria were present in all samples examined. In addition, 68.3% of the lettuce and 71.9% of the fennel samples yielded one or more of the following Salmonella serotypes

Leaves of three or four different ages were taken from olive plants quarterly in 1974-1980. One thousand and fifty isolates of Pseudomonas syringae pv. savastanoi from the phylloplane were tested for virulence to the olive and subjected to numerical phenetic analysis using 60 unit characters. The data were analysed using unweighted average linkage (UPGMA) and single linkage clustering on the simple matching ( S S M ) and pattern (S,) coefficients. The isolates obtained from leaves of a given age at a given time of the year shared higher percentage similarity ( S ) values between themselves than with the others. Cluster composition was only marginally affected by different coefficients and methods of clustering. UPGMA analysis on the S,, coefficient recovered 92% of the isolates in 10 major clusters at 75% S. Of the isolates from leaves of the same age collected at the same time of the year, 81-99% fell in the same cluster. Conversely, 91-97% of the isolates in five of the major clusters were from leaves of the same type. Of the isolates in the other major clusters, 95-98% were from two different sources but most of the isolates from leaves of one type segregated into discrete subclusters at 85-90% S. Hypothetical median organisms (HMOs) were constructed to represent all the isolates obtained from the leaves of each type each year. The resulting relationships between the HMOs confirmed those described above for the individual isolates.

Abstract. Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.