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10 results for “Malurus”
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
Data from: Ornamentation is associated with social costs in male red-backed fairywrens (<em>Malurus melanocephalus</em>)
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Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
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Data from: Geographic patterns of song variation in four species of Malurus fairy-wrens
Geographic variation in song is widespread among birds, particularly in species that learn vocalizations. The relationship between geographic distance and song variation is likely related to the degree of isolation between populations. To assess this effect of geographic isolation on song divergence, we examined patterns of geographic song variation in four species of Australian fairy-wrens (Malurus), two with suspected histories of geographic isolation and two without. Song variation in all four species was consistent with patterns of isolation by distance, and allopatric subspecies in two species were more divergent in song than predicted by distance alone. Each species' pattern was unique, and some interspecific variation could not be explained by geographic distance. These results indicate that patterns of geographic variation can be influenced by more than geographic distance and historical isolation alone. We suggest that morphological constraints, environmental influences, and sexual selection may all contribute to the variation observed for each species.
Data from: Genome of an iconic Australian bird: High-quality assembly and linkage map of the superb fairy-wren (Malurus cyaneus)
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Data from: Geographic patterns of song variation in four species of Malurus fairy-wrens
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Data from: Ageing and senescence across reproductive traits and survival in superb fairy-wrens (Malurus cyaneus)
Why do senescence rates of fitness-related traits often vary dramatically? By considering the full ageing trajectories of multiple traits we can better understand how a species' life-history shapes the evolution of senescence within a population. Here, we examined age-related changes in sex-specific survival, reproduction, and several components of reproduction using a long-term study of a cooperatively-breeding songbird, the superb fairy-wren (Malurus cyaneus). We compared ageing patterns between traits by estimating standardized rates of maturation, the age of onset of senescence, and rates of senescence, while controlling for confounding factors reflecting individual variability in life-history. We found striking differences in ageing and senescence patterns between survival and reproduction, as well as between reproductive traits. In both sexes, survival started to decline from maturity onwards. In contrast, all reproductive traits showed improvements into early adulthood, and many showed little or no evidence of senescence. In females, despite senescence in clutch size, number of offspring surviving to independence did not decline in late life, possibly due to improvements in maternal care with age. Superb fairy-wrens have exceptionally high levels of extra-group paternity, and male extra-group reproductive success showed much greater changes with age than did within-group reproductive success, suggesting that male reproductive ageing is driven by sexual selection. We discuss how the superb fairy-wrens' complex life history may contribute to the disparate ageing patterns across different traits.
Data from: Testosterone regulates CYP2J19-linked carotenoid signal expression in male red-backed fairywrens (Malurus melanocephalus)
<p>Carotenoid pigments produce most red, orange, and yellow colours in vertebrates. This coloration can serve as an honest signal of quality that mediates social and mating interactions, but our understanding of the underlying mechanisms that control carotenoid signal production, including how different physiological pathways interact to shape and maintain these signals, remains incomplete. We investigated the role of testosterone in mediating gene expression associated with a red plumage sexual signal in red-backed fairywrens (<i>Malurus melanocephalus</i>). In this species, males within a single population can flexibly produce either red/black nuptial plumage or female-like brown plumage. Combining correlational analyses with a field-based testosterone implant experiment and qPCR, we show that testosterone mediates expression of carotenoid-based plumage in part by regulating expression of CYP2J19, a ketolase gene associated with ketocarotenoid metabolism and pigmentation in birds. This is the first time that hormonal regulation of a specific genetic locus has been linked to carotenoid production in a natural context, revealing how endocrine mechanisms produce sexual signals that shape reproductive success.</p>
Data from: Ageing and senescence across reproductive traits and survival in superb fairy-wrens (Malurus cyaneus)
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Data from: Testosterone regulates CYP2J19-linked carotenoid signal expression in male red-backed fairywrens (Malurus melanocephalus)
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International Brain Laboratory public data
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OpenNeuro
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