Pathogenic bacteria interact not only with the host organism but most probably also with the resident microbial flora. In the knot disease of the olive tree (Olea europaea), the causative agent is the bacterium Pseudomonas savastanoi pv. savastanoi (Psv). Two bacterial species, namely Pantoea agglomerans and Erwinia toletana, which are not pathogenic and are olive plant epiphytes and endophytes, have been found very often to be associated with the olive knot. We identified the chemical signals that are produced by strains of the three species isolated from olive knot and found that they belong to the N-acyl-homoserine lactone family of QS signals. The luxI/R family genes responsible for the production and response to these signals in all three bacterial species have been identified and characterized. Genomic knockout mutagenesis and in planta experiments showed that virulence of Psv critically depends on QS; however, the lack of signal production can be complemented by wild-type E. toletana or P. agglomerans. It is also apparent that the disease caused by Psv is aggravated by the presence of the two other bacterial species. In this paper we discuss the potential role of QS in establishing a stable consortia leading to a poly-bacterial disease.

SummaryThe correct design of experimental studies, the selection of the appropriate statistical analysis of data and the efficient presentation of results are key to the good conduct and communication of science. The last Guidance for the use and presentation of statistics in Weed Research was published in 1988. Since then, there have been developments in both the scope of research covered by the journal and in the statistical techniques available. This paper addresses the changes in statistics and provides a reference work that will aid researchers in the design and analysis of their work. It will also provide guidance for editors and reviewers. The paper is organised into sections, which will aid the selection of relevant paragraphs, as we recognise that particular approaches require particular statistical analysis. It also uses examples, questions and checklists, so that non-specialists can work towards the correct approach. Statistics can be complex, so knowing when to seek specialist advice is important. The structure and layout of this contribution should help weed scientists, but it cannot provide a comprehensive guide to every technique. Therefore, we provide references to further reading. We would like to reinforce the idea that statistical methods are not a set of recipes whose mindless application is required by convention; each experiment or study may involve subtleties that these guidelines cannot cover. Nevertheless, we anticipate that this paper will help weed scientists in their initial designs for research, in the analysis of data and in the presentation of results for publication.

Due to their peculiar characteristics, seed germination and emergence assays may pose problems for data analysis, due to non-normal error distribution and serial correlation between the numbers of seeds counted on different dates from the same experimental unit (Petri dish, pot, plot). Furthermore, it is necessary to consider viable seeds that have not germinated ⁄ emerged at the end of an experiment (censored observations), as well as late germination ⁄ emergence flushes, that relate to genotypic differences within natural occurring seed populations. Traditional methods of data analysis may not be optimal for dealing with these problems. Therefore, survival analysis may represent an appropriate alternative. In this analysis, the time course of germina-tion ⁄ emergence is described by using a non-parametric step function (Ôgermination functionÕ) and the effect of factors and covariates on Ôgermination functionsÕ is assessed by Accelerated Failure Time regression and expressed in terms of Ôtime ratiosÕ. These parameters measure how a change in the explanatory variables changes (prolongs ⁄ shortens) the time to germination of a seed lot. This paper presents four examples of the application of survival analysis on seed germination ⁄ emergence studies. Results are discussed and compared with those obtained with more traditional techniques.

Time-to-event methods have been proposed in the agricultural sciences, as one of the most suitable options for the analysis of seed germination data. In contrast to traditional linear/nonlinear regression, time-to-event methods can easily account for all statistical peculiarities inherited in germination assays, such as censoring, and they can produce unbiased estimates of model parameters and their standard errors. So far, these methods have only been used in combination with empirical models of germination, which are lacking biological underpinnings. We bridge the gap between statistical requirements and biological understanding by developing a general method that formulates biologically-oriented hydro time (HT), thermal time (TT) and hydrothermal time (HTT) models into a time-to-event framework. HT, TT, and HTT models are widely used for describing seed germination and emergence of plants as affected by the environmental temperature and/or water potential. Owing to their simplicity and the direct biological interpretation of model parameters, these models have become one of the most common tools for both predicting germination as well as understanding the physiology of germination responses to environmental factors. However, these models are usually fitted by using nonlinear regression and, therefore, they fall short of statistical rigor when inference about model parameters is of interest. In this study, we develop HT-to-event, TT-to-event and HTT-to-event models and provide a readily available implementation relying on the package "drc" in the R statistical environment. Examples of usage are also provided and we highlight the possible advantages of this procedure, such as efficiency and flexibility.

The current study investigated the effects of Lactobacillus acidophilus and Bacillus subtilis, used as probiotics, on the microflora, morphology, and morphometry of the gut in organic laying hens. The birds (180 Hy-Line laying hens) were divided into 3 homogenous groups and received a pre-deposition diet from 16 to 20 wk of age and a deposition diet for the remaining 7 months of the experiment. The control group ( CTR: ) was fed a corn-soybean cake-based diet, the second group ( L: ) received the same diet supplemented with 0.1% of L. acidophilus while in the third group ( B: ) the basal diet was supplemented with 0.05% of B. subtilis At 18 wk of age ( T1: ) and at 5 ( T2: ) and 7 months ( T3: ) from the beginning of deposition, 9 subjects per group were humanely killed for microbiological, morphological and morphometric analyses of the intestinal tract. The 2 probiotic-supplemented diets increased Lactobacillus spp. and Bifidobacterium spp. counts compared with the CTR diet. The lowest viable counts of E. coli, coliforms and staphylococci were observed in the L group (P < 0.001). Clostridium spp. decreased (P < 0.001) in both L and B subjects. The probiotic supplementation appeared to affect the intestinal microbial population, promoting the presence of beneficial bacteria such as Lactobacillus spp. and Bifidobacterium spp. and reducing potential harmful bacteria such as E. coli, clostridia and staphylococci. Morphological and morphometric analyses did not reveal substantial differences among groups. At T3, the plasma cell infiltrate in the villi of the CTR hens was more severe than that observed in the L and B groups (P = 0.009).