More generally, predation appears to reverse the evolution of extreme sexually selected phenotypes (reviewed in the ref. This is particularly well documented in orthopterans and frogs 12, 13, 14, 15, 16, 17, 18, and this form of natural selection is probably responsible for the loss of cricket sexual signals on two Hawaiian islands 19, 20. For example, sexual signals are conspicuous to potential mates but may also attract predators and parasitoid 11. One potentially important source of natural selection that could affect the evolution of sexually selected traits is predation 1, and many studies have shown predation can seemingly oppose the exaggeration of male sexual characters. While theory is clear on the joint effects of natural and sexual selection on sexual trait evolution, explicit experimental tests of theoretical predictions are required to fully understand sexual trait evolution 10. They also demonstrate how natural selection can oppose sexual selection as trait values move beyond their naturally selected optima (reviewed in ref. Lande’s 5 and Kirkpatrick’s 6 models of sexual selection via the Fisher 7 process-the null models of intersexual selection 8-clearly shows how this can occur. Sexual selection typically acts more strongly on males and is responsible for the evolution of a vast array of exaggerated characters that enhance male sexual fitness components 1, 2, 3, 4. Our findings support fundamental theory, but also reveal unforseen outcomes-the indirect effect on females-when natural selection targets sex-limited sexually selected characters. Predation solely on females has no effects. We find that populations subjected to male-specific predation evolve smaller sexually selected mandibles and this indirectly increases female fitness, seemingly through intersexual genetic correlations we document. Here we use experimental evolution of replicate populations of broad-horned flour beetles to test for effects of sex-specific predation on an exaggerated sexually selected male trait (the mandibles), while also testing for effects on female lifetime reproductive success. Empirical evidence supports this theory, but to our knowledge, there have been no experimental evolution studies directly testing this logic, and little examination of possible associated effects on female fitness. Its features are modules for statistical data analysis.Theory shows how sexual selection can exaggerate male traits beyond naturally selected optima and also how natural selection can ultimately halt trait elaboration. Presentation functions, including statistical analysis and graphical presentation of data. It can perform a variety of data analyses and This program can be used to analyze dataĬollected from surveys, tests, observations, etc. PASW stands for Predictive Analytics Software. Using Scripting for Redundant Statistical Analyses. Importing/Exporting Microsoft Excel and PowerPoint.68 With Fixed Expected Values and within a Contiguous Subset of Values. Introduction – Part 4.55ĭownloading the Data Files.55Ĭhi-Square. Manipulating the Scales on X- and Y-axes. Regression Analysis.48Īnalyzing the Results.48Ĭhart Editing. Predicting This Year’s Sales with Multiple Regression Model.45ĭata Transformation. Predicting Values of Dependent Variables. Predicting This Year’s Sales with Simple Regression Model.41
Introduction – Part 3.37ĭownloading the Data Files.37 Inserting Variables and Cases.29ĭeleting Variables and Cases. 27Ĭopying and Pasting Variable Properties.27 Introduction – Part 2.18ĭownloading the Data Files.18 6ĭefining Variables.6ĭata Entry.8ĭescriptive Statistics. Introduction – Part 1.4ĭownloading the Data Files.4
Statistics for single dependent variables (lab exercises)įor more assistance, visit BRAINLINK EDUCATIONAL SERVICES Modifying and organizing your data (lab exercises)Ĭompute, recode, and count transformationsįrequencies, descriptives, and simple graphs We will spend about one hour covering basic information about SPSS and getting ready for data entry in a lecture format and two hours in the computer lab with hands-on exercises in using SPSS.Ĭreating a codebook – variables, names, labels, missing valuesĮntering data in SPSS (lecture and lab exercises) Descriptive statistics, chi-square, means comparisons (t-test and one-way ANOVA) and correlations will be covered.
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Hands-on lab exercises will demonstrate how to execute basic data transformations and analyses using SPSS. This workshop will provide an overview of the statistical test choices, variable creation and transformation, and output interpretation using SPSS.