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      Two sides of a coin: ecological and chronobiological perspectives of timing in the wild

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          Climate change and population declines in a long-distance migratory bird.

          Phenological responses to climate change differ across trophic levels, which may lead to birds failing to breed at the time of maximal food abundance. Here we investigate the population consequences of such mistiming in the migratory pied flycatcher, Ficedula hypoleuca. In a comparison of nine Dutch populations, we find that populations have declined by about 90% over the past two decades in areas where the food for provisioning nestlings peaks early in the season and the birds are currently mistimed. In areas with a late food peak, early-breeding birds still breed at the right time, and there is, at most, a weak population decline. If food phenology advances further, we also predict population declines in areas with a late food peak, as in these areas adjustment to an advanced food peak is insufficient. Mistiming as a result of climate change is probably a widespread phenomenon, and here we provide evidence that it can lead to population declines.
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            Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

            Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data. © 2010 The Authors. Biological Reviews © 2010 Cambridge Philosophical Society.
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              Evolutionary Significance of Phenotypic Plasticity in Plants

               A.D. Bradshaw (1965)
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                Author and article information

                Journal
                Philosophical Transactions of the Royal Society B: Biological Sciences
                Phil. Trans. R. Soc. B
                The Royal Society
                0962-8436
                1471-2970
                October 09 2017
                October 09 2017
                : 372
                : 1734
                : 20160246
                Article
                10.1098/rstb.2016.0246
                28993490
                © 2017

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