Network structure embracing mutualism–antagonism continuums increases community robustness

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Abstract

Theory predicts that contrasting properties of mutualistic and antagonistic networks differentially promote community resilience to species loss. However, the outcome of most ecological interactions falls within a continuum between mutualism and antagonism, and we ignore the extent to which this interactions' continuum might influence community stability. Using a large data set of interactions, we compared co-extinction cascades that either consider or ignore the mix of beneficial and detrimental actions that parrots exert on plants. When the antagonism-mutualism continuum was considered, a combination of the properties that separately enhance community stability in ecological networks emerged. This combination of properties led to an overall increase of the parrot community robustness to face plant species loss. Our results highlight that the conditional outcomes of interactions can influence the structure of ecological networks, thus affecting our predictions of community stability against eventual changes.

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

Shinichi Nakagawa, Innes Cuthill (2007)

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.