Estimating egg mass-body mass relationships in birds

Abstract The mass of a bird’s egg is a critical attribute of the species’ life history and represents a fundamental component of reproductive effort. Indeed, the tradeoff between the number of eggs in a clutch and clutch mass lies at the heart of understanding how environmental attributes such as nest predation or adult mortality influence reproductive investment. However, egg masses have not been reported for the majority of avian species. We capitalized on the strong allometric relationship between avian body mass and egg mass to produce egg mass estimates for over 5,500 species previously lacking such information. These estimates are accompanied by measures of the robustness of the regressions used to produce them (e.g., sample size, root mean square error [RMSE] of estimation, coefficient of determination, and degree of extrapolation), thus allowing independent evaluation of the suitability of any estimate to address a particular research question relating to avian life history. Most estimates (~5,000) were based on family-level egg mass–body mass regressions, with the remainder derived from other relationships such as ordinal regressions. We compared estimating regressions based on adult vs. female body masses and, after finding little difference between the 2, based our final estimates on adult masses as those were more numerous in the literature. What small differences between adult- and female-based regressions that did occur were not related to sexual size dimorphism across families. These new estimates, coupled with ~5,000 egg masses reported in the literature, provide a foundation of over 10,000 species for wider investigations assessing variation in reproductive effort in birds over a broad array of ecological and evolutionary contexts

Introduction to Principal Components Analysis

Understanding the inverse equivalent width - luminosity relationship (Baldwin Effect), the topic of this meeting, requires extracting information on continuum and emission line parameters from samples of AGN. We wish to discover whether, and how, different subsets of measured parameters may correlate with each other. This general problem is the domain of Principal Components Analysis (PCA). We discuss the purpose, principles, and the interpretation of PCA, using some examples from QSO spectroscopy. The hope is that identification of relationships among subsets of correlated variables may lead to new physical insight.Comment: Invited review to appear in ``Quasars and Cosmology'', A.S.P. Conference Series 1999. eds. G. J. Ferland, J. A. Baldwin, (San Francisco: ASP). 10 pages, 2 figure

Factor Analysis of Mixed Data (FAMD) and Multiple Linear Regression in R

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal. In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second group analyses the predictors more relate to a family situation or family relationships. The approach to constructing the linear model is using the principal component results from the analyses of the principal component instead of the original features or predictors. The linear model proposal is: score G3 = a + b1*(PC1) + b2*(PC2) + ... + bk*(PCk) bi = Coefficients PCi = principal component, i: 1, 2, …, k dimensions Due that the variables are numeric and categorical, it will be used the extension method called Factor Analysis of Mixed Data (FAMD) to deal with data quantitative and data qualitative

Simple data analysis for biologists

This document provides a simple introduction to research methods and analysis tools for biologists or environmental scientists, with particular emphasis on fish biology in devleoping countries

Nanopipettes as Monitoring Probes for the Single Living Cell: State of the Art and Future Directions in Molecular Biology.

Examining the behavior of a single cell within its natural environment is valuable for understanding both the biological processes that control the function of cells and how injury or disease lead to pathological change of their function. Single-cell analysis can reveal information regarding the causes of genetic changes, and it can contribute to studies on the molecular basis of cell transformation and proliferation. By contrast, whole tissue biopsies can only yield information on a statistical average of several processes occurring in a population of different cells. Electrowetting within a nanopipette provides a nanobiopsy platform for the extraction of cellular material from single living cells. Additionally, functionalized nanopipette sensing probes can differentiate analytes based on their size, shape or charge density, making the technology uniquely suited to sensing changes in single-cell dynamics. In this review, we highlight the potential of nanopipette technology as a non-destructive analytical tool to monitor single living cells, with particular attention to integration into applications in molecular biology