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      Mixed-species flocking is associated with low arthropod detectability and increased foraging efficiency by Yungas forest birds in Argentina

      1 , 2 , 1 , 3 , 3
      Ornithology
      Oxford University Press (OUP)

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          Abstract

          Mixed-species flocks presumably provide birds with antipredator and foraging benefits. The foraging benefits hypothesis predicts that a reduction in arthropod abundance will trigger flocking activity; however, flocking activity may also be influenced by the difficulty of detecting arthropods, a seldom explored possibility. We found that environmental traits (temperature and foliage density) combined with arthropod abundance explained arthropod detection by birds in the Yungas foothill forest of NW Argentina. Prey detection was inversely related to ambient temperature and foliage density while positively associated with arthropod abundance. Based on this result, we built a structural equation model using a latent proxy variable for arthropod detectability, arthropod crypsis, integrating ambient temperature, foliage density, and proportion of immature arthropods. This model allowed us to compare the relative importance of arthropod abundance and the difficulty in detecting prey items as predictors of flocking propensity. After 2 yr of studying 129 mixed-species flocks, 1,351 bird foraging sequences, and 25,591 arthropod captures, we found that the flocking propensity of birds was only significantly correlated with arthropod detectability and not with arthropod abundance. Flocking propensity peaked when the arthropod community was comprised of proportionately more immature and non-flying arthropods, the temperature was low, and the foliage cover was denser; all factors are contributing to a low arthropod detectability. Finally, we evaluated whether joining mixed-species flocks provided foraging benefits such as increased foraging efficiency. Individuals benefited from joining flocks by an average increase of their prey-capture attempt rate of 40%, while the search rate increased by 16%. Our results add a new perspective on the drivers of mixed-species flocking by showing that the capacity to find prey items may have a more significant effect than prey abundance per se.

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          lavaan: AnRPackage for Structural Equation Modeling

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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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              Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review

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                Author and article information

                Journal
                Ornithology
                Oxford University Press (OUP)
                0004-8038
                2732-4613
                April 08 2022
                March 25 2022
                February 21 2022
                April 08 2022
                March 25 2022
                February 21 2022
                : 139
                : 2
                Affiliations
                [1 ]Instituto de Ecología Regional, Universidad Nacional de Tucumán (UNT) - Consejo Nacional de Investigaciones Científcas y Técnicas (CONICET), Tucumán, Argentina
                [2 ]Departamento de Ecologia, Unesp - Campus Rio Claro, Rio Claro, SP, Brasil
                [3 ]Laboratorio de Ecología, Comportamiento y Sonidos Naturales (ECOSON), Instituto de Bio y Geociencias del Noroeste Argentino-CONICET, Salta, Argentina
                Article
                10.1093/ornithology/ukab087
                340e9f53-5f51-4ceb-a9bf-0734b61084a9
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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