The use of a multivariate statistical procedure in analysing the germination process of two bean cultivars, compared with a univariate approach

Abstract Several studies on plant physiology are aimed at describing or assessing seed germination processes under laboratory conditions. With respect to seed germination of crop species, some statistical complexities have been discussed, but they have not been developed much in practice. That is, such discussions are not as common as in other areas of plant biology. Additionally, the current literature that is concerned directly with the application of statistics in seed germination indicates that simple and well-known statistical procedures still merit further consideration. Regarding the use of multivariate statistical methods in agriculture, several field studies have used such procedures as a means of clarifying some underlying ecological principles that govern crop production. Nonetheless, multivariate tests have not been widely employed in germination experiments. Therefore, in the present study a simple multivariate statistical procedure (Hotelling's T 2 statistic) was utilised in order to compare two common bean cultivars, using germination indices as variables. The outcome derived from the multivariate approach was compared with that obtained from the utilisation of the univariate t test. The simultaneous application of both methods (that is, the classical univariate t test and the multivariate T 2 test) showed that the outcomes may well depend on the approach utilised

Dose-related effects of alcohol on cognitive functioning

We assessed the suitability of six applied tests of cognitive functioning to provide a single marker for dose-related alcohol intoxication. Numerous studies have demonstrated that alcohol has a deleterious effect on specific areas of cognitive processing but few have compared the effects of alcohol across a wide range of different cognitive processes. Adult participants (N = 56, 32 males, 24 females aged 18–45 years) were randomized to control or alcohol treatments within a mixed design experiment involving multiple-dosages at approximately one hour intervals (attained mean blood alcohol concentrations (BACs) of 0.00, 0.048, 0.082 and 0.10%), employing a battery of six psychometric tests; the Useful Field of View test (UFOV; processing speed together with directed attention); the Self-Ordered Pointing Task (SOPT; working memory); Inspection Time (IT; speed of processing independent from motor responding); the Traveling Salesperson Problem (TSP; strategic optimization); the Sustained Attention to Response Task (SART; vigilance, response inhibition and psychomotor function); and the Trail-Making Test(TMT; cognitive flexibility and psychomotor function). Results demonstrated that impairment is not uniform across different domains of cognitive processing and that both the size of the alcohol effect and the magnitude of effect change across different dose levels are quantitatively different for different cognitive processes. Only IT met the criteria for a marker for wide-spread application: reliable dose-related decline in a basic process as a function of rising BAC level and easy to use non-invasive task properties.Mathew J. Dry, Nicholas R. Burns, Ted Nettelbeck, Aaron L. Farquharson and Jason M. Whit

Estimating a resource selection function with line transect sampling

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice