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Multivariate Trade-off Presence
No full textPreregistration for methods to be used in reanalyzing the data from Chang et al. 2023. Preregistration also documents a priori criteria for determining presence of a trade-off
Comparing ecological and evolutionary variability within datasets
No full textMany key questions in evolutionary ecology require the use of variance ratios such as heritability, repeatability, and individual resource specialization. These ratios allow to understand how phenotypic variation is structured into genetic and non-genetic components, to identify how much organisms vary in the resources they use or how functional traits structure species communities. Understanding how evolutionary and ecological processes differs among populations and environments therefore often requires the comparison of these ratios across groups (i.e. populations, sexes, species). Inference based on comparisons of ratios can be limited, however. Variance ratios can remain the same across group despite very different values in the numerator and denominator variances. Moreover, evolutionary ecologists are most often interested in differences in specific variance component among groups rather than in differences in variance ratios per se. Recommendations for how to infer whether groups differ in variance are not clear in the literature. Using simulations, we show how questions regarding the estimation of variance components and their differences among groups can be answered with Hierarchical Linear Modeling approaches (HLMs). Frequentist and Bayesian frameworks have similar abilities to identify differences in variance components. However, variance differences at higher levels of organization (i.e. the among-unit variance) can be difficult to detect with low sample sizes. We provide tools to conduct power analyses to determine the appropriate sample sizes necessary to detect differences in variance of a given magnitude. We conclude by supplying guidelines for how to report and draw inferences based on the comparisons of variance components and variance ratios
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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