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      Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird

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          Flexibility in scheduling varies throughout an organism’s annual cycle, reflecting relative temporal constraints and fitness consequences among life-history stages. Time-selection can act at different scales, either by limiting the range of alternative strategies in the population, or by increasing the precision of individual performance. We tracked individual bar-tailed godwits Limosa lapponica baueri for two full years (including direct observation during non-breeding seasons in New Zealand and geolocator tracking of round-trip migrations to Alaska) to present a full annual-cycle view of molt, breeding, and migration schedules. At both population and individual scales, temporal variation was greater in post-breeding than pre-breeding stages, and greater in molts than in movements, but schedules did not tighten across successive stages of migration toward the breeding grounds. In general, individual godwits were quite consistent in timing of events throughout the year, and repeatability of pre-breeding movements was particularly high ( r = 0.82–0.92). However, we demonstrate that r values misrepresent absolute consistency by confounding inter- and intra-individual variation; the biological significance of r values can only be understood when these are considered separately. By doing so, we show that some stages have considerable tolerance for alternative strategies within the population, whereas scheduling of northbound migratory movements was similar for all individuals. How time-selection simultaneously shapes both individual and population variation is central to understanding and predicting adaptive phenological responses to environmental change.

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          Most cited references 14

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          Ecological responses to recent climate change.

          There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.
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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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              Carry-over effects as drivers of fitness differences in animals.

              1. Carry-over effects occur when processes in one season influence the success of an individual in the following season. This phenomenon has the potential to explain a large amount of variation in individual fitness, but so far has only been described in a limited number of species. This is largely due to difficulties associated with tracking individuals between periods of the annual cycle, but also because of a lack of research specifically designed to examine hypotheses related to carry-over effects. 2. We review the known mechanisms that drive carry-over effects, most notably macronutrient supply, and highlight the types of life histories and ecological situations where we would expect them to most often occur. We also identify a number of other potential mechanisms that require investigation, including micronutrients such as antioxidants. 3. We propose a series of experiments designed to estimate the relative contributions of extrinsic and intrinsic quality effects in the pre-breeding season, which in turn will allow an accurate estimation of the magnitude of carry-over effects. To date this has proven immensely difficult, and we hope that the experimental frameworks described here will stimulate new avenues of research vital to advancing our understanding of how carry-over effects can shape animal life histories. 4. We also explore the potential of state-dependent modelling as a tool for investigating carry-over effects, most notably for its ability to calculate optimal rates of acquisition of a multitude of resources over the course of the annual cycle, and also because it allows us to vary the strength of density-dependent relationships which can alter the magnitude of carry-over effects in either a synergistic or agonistic fashion. 5. In conclusion carry-over effects are likely to be far more widespread than currently indicated, and they are likely to be driven by a multitude of factors including both macro- and micronutrients. For this reason they could feasibly be responsible for a large amount of the observed variation in performance among individuals, and consequently warrant a wealth of new research designed specifically to decompose components of variation in fitness attributes related to processes across and within seasons. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

                Author and article information

                Ecology Group, Institute of Natural Resources, Massey University, Palmerston North, New Zealand
                University of Western Ontario, Canada
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JRC PFB. Performed the experiments: JRC. Analyzed the data: JRC. Wrote the paper: JRC PFB MAP.


                Current address: Animal Ecology Group, University of Groningen, Groningen, Netherlands

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                16 January 2013
                : 8
                : 1

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Pages: 9
                This project was supported by the David and Lucile Packard Foundation through the Pacific Shorebird Migration Project, and by a Marsden Fund grant administered by The Royal Society of New Zealand to P. Battley. Color-banding was conducted under Department of Conservation Research and Development contracts 3739-01 and 3599. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Behavioral Ecology
                Population Ecology
                Terrestrial Ecology
                Evolutionary Biology
                Animal Behavior
                Animal Behavior



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