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How should we interpret estimates of individual repeatability?

ABSTRACT: Individual repeatability (R), defined as the proportion of observed variance attributable to among-individual differences, is a widely used summary statistic in evolutionarily motivated studies of morphology, life history, physiology and, especially, behaviour. Although statistical methods to estimate R are well known and widely available, there is a growing tendency for researchers to interpret R in ways that are subtly, but importantly, different. Some view R as a property of a dataset and a statistic to be interpreted agnostically with respect to mechanism. Others wish to isolate the contributions of 'intrinsic' and/or 'permanent' individual differences, and draw a distinction between true (intrinsic) and pseudo-repeatability arising from uncontrolled extrinsic effects. This latter view proposes a narrower, more mechanistic interpretation, than the traditional concept of repeatability, but perhaps one that allows stronger evolutionary inference as a consequence (provided analytical pitfalls are successfully avoided). Neither perspective is incorrect, but if we are to avoid confusion and fruitless debate, there is a need for researchers to recognise this dichotomy, and to ensure clarity in relation to how, and why, a particular estimate of R is appropriate in any case.

SUBMITTER: Wilson AJ

PROVIDER: S-EPMC6121803 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

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How should we interpret estimates of individual repeatability?

Wilson Alastair J AJ

Evolution letters 20180131 1

Individual repeatability (R), defined as the proportion of observed variance attributable to among-individual differences, is a widely used summary statistic in evolutionarily motivated studies of morphology, life history, physiology and, especially, behaviour. Although statistical methods to estimate R are well known and widely available, there is a growing tendency for researchers to interpret R in ways that are subtly, but importantly, different. Some view R as a p ...[more]

PMID: 30283660

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Project description:Changes in phenology and distribution are being widely reported for many migratory species in response to shifting environmental conditions. Understanding these changes and the situations in which they occur can be aided by understanding consistent individual differences in phenology and distribution and the situations in which consistency varies in strength or detectability. Studies tracking the same individuals over consecutive years are increasingly reporting migratory timings to be a repeatable trait, suggesting that flexible individual responses to environmental conditions may contribute little to population-level changes in phenology and distribution. However, how this varies across species and sexes, across the annual cycle and in relation to study (tracking method, study design) and/or ecosystem characteristics is not yet clear. Here, we take advantage of the growing number of publications in movement ecology to perform a phylogenetic multilevel meta-analysis of repeatability estimates for avian migratory timings to investigate these questions. Of 2,433 reviewed studies, 54 contained suitable information for meta-analysis, resulting in 177 effect sizes from 47 species. Individual repeatability of avian migratory timings averaged 0.414 (95% confidence interval: 0.3-0.5) across landbirds, waterbirds and seabirds, suggesting consistent individual differences in migratory timings is a common feature of migratory systems. Timing of departure from the non-breeding grounds was more repeatable than timings of arrival at or departure from breeding grounds, suggesting that conditions encountered on migratory journeys and outcome of breeding attempts can influence individual variation. Population-level shifts in phenology could arise through individual timings changing with environmental conditions and/or through shifts in the numbers of individuals with different timings. Our findings suggest that, in addition to identifying the conditions associated with individual variation in phenology, exploring the causes of between-individual variation will be key in predicting future rates and directions of changes in migratory timings. We therefore encourage researchers to report the within- and between- individual variance components underpinning the reported repeatability estimates to aid interpretation of migration behaviour. In addition, the lack of studies in the tropics means that levels of repeatability in less strongly seasonal environments are not yet clear.

Project description:Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

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How should we interpret estimates of individual repeatability?

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How should we interpret estimates of individual repeatability?

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