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      Social modulation of androgens in male birds.

      General and Comparative Endocrinology
      Androgens, metabolism, Animals, Behavior, Animal, Birds, physiology, Female, Male, Social Behavior, Territoriality, Testosterone

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          Abstract

          Most seasonally reproducing vertebrates show pronounced changes in testosterone levels throughout the year. The Challenge Hypothesis [Wingfield, J.C., Hegner, R.E., Dufty, A.M., Ball, G. F., 1990. The "challenge hypothesis": theoretical implications for patterns of testosterone secretion, mating systems, and breeding strategies. Am. Nat. 136, 829-846] predicts that seasonal patterns in circulating androgen concentrations vary as a function of mating system, male-male aggression and paternal care. In most comparative studies, the predictions of the Challenge Hypothesis have been tested primarily by calculating the ratio between breeding peak and breeding baseline testosterone concentrations, using this ratio as a proxy for the effect that social interactions have on testosterone levels (androgen responsiveness R). Recently, we suggested that it is preferable to separate the seasonal testosterone response (R(season)) from the androgen responsiveness to male-male interactions (R(male-male)), as these two measures do not correlate and can differ both in magnitude and direction [Goymann, W., Landys, M.M., Wingfield, J.C., 2007. Distinguishing seasonal androgen responses from male-male androgen responsiveness-revisiting the Challenge Hypothesis. Horm. Behav. 51, 463-476]. Here, I discuss several methodological and ecological factors that may explain why R(season) and R(male-male) differ. Furthermore, I describe three other kinds of androgen responsiveness, namely the androgen responsiveness of males to receptive females (R(male-female)), to non-social environmental cues (R(environment)), and the potential androgen responsiveness (R(potential)). The latter is measured before and after an injection of gonadotropin releasing hormone (GnRH), which typically leads to a maximal release of testosterone from the testes. I argue that separation of different kinds of androgen responsiveness and putting them into context with the natural history and ecology of a study species may help to better understand variations in androgen responsiveness to social and non-social environmental factors. On an ultimate level this may help to better understand the benefits and costs of increasing, or not increasing testosterone concentrations during social interactions. Proximately, this will aid in a more complete understanding of the mechanisms by which testosterone regulates behavioral traits and by which behavior feeds back on hormone levels.

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Global Climate Change Leads to Mistimed Avian Reproduction

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              Tits and their caterpillar food supply

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                Author and article information

                Journal
                19100740
                10.1016/j.ygcen.2008.11.027

                Chemistry
                Androgens,metabolism,Animals,Behavior, Animal,Birds,physiology,Female,Male,Social Behavior,Territoriality,Testosterone

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