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Asymptotic Size (asymptotic + size)
Selected AbstractsAge-based life history parameters and status assessments of by-catch species (Lethrinus borbonicus, Lethrinus microdon, Pomacanthus maculosus and Scolopsis taeniatus) in the southern Arabian GulfJOURNAL OF APPLIED ICHTHYOLOGY, Issue 3 2010E. Grandcourt Summary Life history and demographic parameters for Lethrinus borbonicus, Lethrinus microdon, Pomacanthus maculosus and Scolopsis taeniatus in the southern Arabian Gulf were estimated using a combination of size frequency, biological and size-at-age data. Defined structural increments consisting of alternating translucent and opaque bands in transverse sections of sagittal otoliths were validated as annuli. The maximum age estimates ranged from 5 years for Scolopsis taeniatus to 36 years for Pomacanthus maculosus. The size-at-age relationships were highly asymptotic in form with the majority of growth being achieved early in life. There were significant differences in the growth characteristics between sexes for Pomacanthus maculosus, with males approaching a larger asymptotic size at a faster rate than females. With the exception of Scolopsis taeniatus, the mean age at which fish became vulnerable to capture was lower than the mean age at first sexual maturity. The stocks of L. microdon, P. maculosus and S. taeniatus were exploited within sustainable limits, conversely, L. borbonicus was found to be overexploited and recruitment overfishing may have occurred as the relative spawner biomass per recruit was below 30% of the unexploited state. [source] Are parametric models suitable for estimating avian growth rates?JOURNAL OF AVIAN BIOLOGY, Issue 4 2007William P. Brown For many bird species, growth is negative or equivocal during development. Traditional, parametric growth curves assume growth follows a sigmoidal form with prescribed inflection points and is positive until asymptotic size. Accordingly, these curves will not accurately capture the variable, sometimes considerable, fluctuations in avian growth over the course of the trajectory. We evaluated the fit of three traditional growth curves (logistic, Gompertz, and von Bertalanffy) and a nonparametric spline estimator to simulated growth data of six different specified forms over a range of sample sizes. For all sample sizes, the spline best fit the simulated model that exhibited negative growth during a portion of the trajectory. The Gompertz curve was the most flexible for fitting simulated models that were strictly sigmoidal in form, yet the fit of the spline was comparable to that of the Gompertz curve as sample size increased. Importantly, confidence intervals for all of the fitted, traditional growth curves were wholly inaccurate, negating the apparent robustness of the Gompertz curve, while confidence intervals of the spline were acceptable. We further evaluated the fit of traditional growth curves and the spline to a large data set of wood thrush Hylocichla mustelina mass and wing chord observations. The spline fit the wood thrush data better than the traditional growth curves, produced estimates that did not differ from known observations, and described negative growth rates at relevant life history stages that were not detected by the growth curves. The common rationale for using parametric growth curves, which compress growth information into a few parameters, is to predict an expected size or growth rate at some age or to compare estimated growth with other published estimates. The suitability of these traditional growth curves may be compromised by several factors, however, including variability in the true growth trajectory. Nonparametric methods, such as the spline, provide a precise description of empirical growth yet do not produce such parameter estimates. Selection of a growth descriptor is best determined by the question being asked but may be constrained by inherent patterns in the growth data. [source] Parent age differentially influences offspring size over the course of development in Laysan albatrossJOURNAL OF ZOOLOGY, Issue 1 2008D. C. Dearborn Abstract Offspring growth and survival are predicted to be higher for older parents, due to a variety of mechanisms, such as increased breeding experience or greater investment favored by low residual reproductive value. Yet the extent to which parent age affects offspring viability is likely to vary between different aspects of growth and survival, perhaps being most pronounced at the most stressful stages of reproduction. We studied the link between parent age and nestling growth and survival in the Laysan albatross, a long-lived seabird with a mean first breeding age of 8 years. Offspring of older parents were more likely to survive to fledging. Among those that did fledge, nestlings of older parents grew more rapidly. However, parent age did not influence the eventual asymptotic size that nestlings reached before fledging: fast-growing nestlings of older parents reached 90% of asymptotic size roughly 1 week sooner, but slow-growing nestlings of younger parents eventually caught up in size before fledging. Older parents bred c. 2 days earlier than younger parents, but hatch date did not explain observed variation in offspring success. The extent to which parent age accounted for variation in size of individual nestlings was not constant but peaked near the midpoint of development. This could reflect a time period when demands on parents reveal age-based differences in parental quality. Overall, growth and survival of offspring increased with parent age in this species, even though the late age of first breeding potentially provides a 7-year period for birds to hone their foraging skills or for selection to eliminate low-quality individuals. [source] Change-point monitoring in linear modelsTHE ECONOMETRICS JOURNAL, Issue 3 2006Alexander Aue Summary, We consider a linear regression model with errors modelled by martingale difference sequences, which include heteroskedastic augmented GARCH processes. We develop asymptotic theory for two monitoring schemes aimed at detecting a change in the regression parameters. The first method is based on the CUSUM of the residuals and was studied earlier in the context of independent identically distributed errors. The second method is new and is based on the squares of prediction errors. Both methods use a training sample of size m. We show that, as m,,, both methods have correct asymptotic size and detect a change with probability approaching unity. The methods are illustrated and compared in a small simulation study. [source] | ||||||||||