Mario Miguel Ojeda

Analysis of Variance for Random Models

Analysis of variance for random models , Analysis of variance for random models , کتابخانه دیجیتال جندی شاپور اهواز

Novel Analysis of Spatial and Temporal Patterns of Resource Use in a Group of Tephritid Flies of the Genus Anastrepha

Abstract The spatial and temporal patterns of oviposition-resource use of various Anastrepha spp. fruit flies within the canopies of individual fruit trees were determined over periods of 4–6 yr in the state of Veracruz, Mexico. The flies examined were Anastrepha obliqua (Macquart), Anastrepha striata Schiner, Anastrepha fracterculus (Wiedemann), and Anastrepha alveata Stone, and their respective hosts were Spondias mombin L. (Anacardiaceae), Psidium guajava L., Psidium sartorianum (Berg.) Ndzu (Myrtacaea), and Ximenia americana L. (Olacaceae). The canopies were divided into six sectors: three strata (vertical planes of low, middle, and high canopy) and an exterior and interior component of the various heights. All ripe fruits produced by each tree species were individually harvested, weighed, and maintained until all larvae had exited and pupated. Because of the commonly positive correlation between fruit size and infestation, fly distributions were described using a novel technique, two-level hierarchal regression analysis, as deviations from the expected numbers of insects in a sector given the distributions of fruit weights within the canopy. Overall, there was a tendency for A. alveata to be more abundant in the lower portions of the tree, for A. striata to be more abundant in the upper, for A. obliqua to be less abundant in the upper, and for A. fraterculus to be uniformly distributed. The yearly densities of A. striata and A. fraterculus within the P. guajava tree were negatively correlated, and this seems to be due to annual changes in environment rather than to exploitive competition for oviposition resources. Along an altitudinal gradient (0–1,800 m), A. striata was more abundant than A. fraterculus at sea level and relatively less abundant at altitudes of 1000 m and higher. We suggest that habitat characteristics (oviposition-resource availability and quality, and microclimatic variables), intraspecific competition, and the behaviors of natural enemies and frugivores are potentially important interactive factors that influence the distribution of resource use to a different extent in each of the tephritid species.

Specialization clines in the pollination systems of agaves (Agavaceae) and columnar cacti (Cactaceae): a phylogenetically controlled meta-analysis.

The biogeography of plant-animal interactions is a novel topic on which many disciplines converge (e.g., reproductive biology, biogeography, and evolutionary biology). Narrative reviews have indicated that tropical columnar cacti and agaves have highly specialized pollination systems, while extratropical species have generalized systems. However, this dichotomy has never been quantitatively tested. We tested this hypothesis using traditional and phylogenetically informed meta-analysis. Three effect sizes were estimated from the literature: diurnal, nocturnal, and hand cross-pollination (an indicator of pollen limitation). Columnar cactus pollination systems ranged from purely bat-pollinated in the tropics to generalized pollination, with diurnal visitors as effective as nocturnal visitors in extratropical regions; even when phylogenetic relatedness among species is taken into account. Metaregressions identified a latitudinal increase in pollen limitation in columnar cacti, but this increase was not significant after correcting for phylogeny. The currently available data for agaves do not support any latitudinal trend. Nectar production of columnar cacti varied with latitude. Although this variation is positively correlated with pollination by diurnal visitors, it is influenced by phylogeny. The degree of specificity in the pollination systems of columnar cacti is heavily influenced by ecological factors and has a predictable geographic pattern.

Problems and challenges of teaching biostatistics to medical students and professionals

The problems and challenges encountered in teaching biostatistics to medical students and professionals are considered. Some suggestions and tips, which may help to overcome some of these problems and enhance certain aspects of biostatistics teaching and learning, are presented.

An Applied Statistics Course for Systematics and Ecology PhD Students

Statistics education is under review at all educational levels. Statistical concepts, as well as the use of statistical methods and techniques, can be taught in at least two contrasting ways. Specifically, (1) teaching can be theoretically and mathematically oriented, or (2) it can be less mathematically oriented being focused, instead, on application and the use of data to solve real-world problems. The second approach is growing in practice and new goals have recently emerged. At present, statistics courses stress probability concepts, data analysis, and the interpretation and communication of results. Understanding the process of statistical investigation is established as a way of improving mastery of statistical reasoning. In this context, a project-based approach allows the design and implementation of participating learning scenarios in order to understand the statistical methodology and, as a consequence, improve research. This approach points out that statistics is a rational methodology used to solve practical problems. The purpose of this paper is to present the design and results of an applied statistics course for PhD students in ecology and systematics using a project-based approach. Examples involving character coding, species classification, and the interpretation of geographical variation, which are the principal systematic analyses requiring statistical techniques, are presented using the results from student projects. In addition, an example from conservation ecology is presented. Results indicate that the students understood the concepts and applied the systematic and statistical techniques accurately using a data oriented approach.

Design-based Sample and Probability Law-Assumed Sample: Their Role in Scientific Investigation.

Students in statistics service courses are frequently exposed to dogmatic approaches for evaluating the role of randomization in statistical designs, and inferential data analysis in experimental, observational and survey studies. In order to provide an overview for understanding the inference process, in this work some key statistical concepts in probabilistic and nonprobabilistic sampling are discussed. The statistical model constituting the basis of statistical inference is postulated and a brief review of the finite population descriptive inference and a quota sampling inferential theory are provided. Some comments on distinct approaches for conducting inferences in probabilistic and nonprobabilistic samples are adduced.

A two-level regression model for growth curves with correlated outcomes

We propose a mixed-effect linear model, as a particular case of the two-level regression model, for analyzing repeated measures made at completely irregular time points. The model allows for subject-level covariates, so as to study the trend and the variability of the individual growth curves. Application of this model is illustrated on a published data set.

Biplot display for diagnostic in a two-level regression model for growth curve analysis

Abstract This work presents a formulation of the hierarchical general linear model with two levels for growth curve modelling. This formulation considers a K th degree polynomial equation for each individual growth curve like a one-level regression equation. In the second level the regression coefficients are modelled considering q explanatory variables at the individual level, this procedure permits an explication of variability between growth curves. The second level of the model is expressed as a multivariate linear regression model, obtaining a multivariate residuals matrix for the second level. The biplot is proposed as a graphical tool that permits to do diagnostics using this residual matrix. An example of yield growth curves of an orthogonal centered design matrix on a randomized blocks experimental design is used for presenting an illustration of these proposals.

Two-Way Nested Classification

In the preceding two chapters, we have considered experimental situations where the levels of two factors are crossed. In this and the following chapter we onsider experiments where the levels of one of the factors are nested within the levels of the other factor. The data for a two-way nested classification are similar hat of a single factor classification except that now replications are grouped into different sets arising from the levels of the nested factor for a given level of the main factor. Suppose the main factor A has a levels and the nested factor B has ab levels which are grouped into a sets of b levels each, and n observations are made at each level of the factor B giving a total of abn observations. The nested or hierarchical designs of this type are very important in many industrial and genetic investigations. For example, suppose an experiment is designed to investigate the variability of a certain material by randomly selecting a batches, b samples are made from each batch, and finally n analyses are performed on each sample. The purpose of the investigation may be to make inferences about the relative contribution of each source of variation to the total variance or to make inferences about the variance components individually. For another example, suppose in a breeding experiment a random sample of a sires is taken, each sire is mated to a sample of b dams, and finally n offspring are produced from each sire-dam mating. Again, the purpose of the investigation may be to study the relative magnitude of the variance components or to make inferences about them individually.

General Balanced Random Effects Model

In previous chapters, we have considered random effects models for various crossed and nested designs with equal numbers in all cells and subclasses, and which are called balanced complete models. In this chapter, we present a unified treatment of balanced random effects models in terms of the so-called general linear model.