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Simulated Populations (simulated + population)
Selected AbstractsFixed or random contemporary groups in genetic evaluation for litter size in pigs using a single trait repeatability animal modelJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2003D. Babot Summary The importance of using fixed or random contemporary groups in the genetic evaluation of litter size in pigs was analysed by using farm and simulated data. Farm data were from four Spanish pig breeding populations, two Landrace (13 084 and 13 619 records) and two Large White (2762 and 8455 records). A simulated population (200 sows and 10 boars) selected for litter size, in which litter size was simulated using a repeatability animal model with random herd,year,season (HYS), was used to obtain simulated data. With farm data, the goodness-of-fit and the predictive ability of a repeatability animal model were calculated under several definitions of the HYS effect. A residual maximum likelihood estimator of the HYS variance in each population was obtained as well. In this sense, HYS was considered as either fixed or random with different number of individuals per level. Results from farm data showed that HYS variance was small in relation to the total variance (ranging from 0.01 to 0.04). The treatment of HYS effect as fixed, reduced the residual variance but the size of HYS levels does not explain by itself the goodness-of-fit of the model. The results obtained by simulation showed that the predictive ability of the model is better for random than for fixed HYS models. However, the improvement of predictive ability does not lead to a significant increase of the genetic response. Finally, results showed that random HYS models biased the estimates of genetic response when there is an environmental trend. Zusammenfassung Fixe oder zufällige Vergleichsgruppen bei der Zuchtwertschätzung für Wurfgröße beim Schwein mit einem Wiederholbarkeits-Tiermodell Der Einfluss von fixen oder zufälligen Vergleichgruppen bei der Zuchtwertschätzung für Wurfgröße beim Schwein wurde an realen Betriebsdaten und an simulierten Daten untersucht. Die Betriebsdaten stammen von vier spanischen Zuchtpopulationen, zwei Landrasse Populationen (13084 und 13619 Datensätze) und zwei Large White Populationen (2762 und 8455 Datensätze). Für die Simulation wurde eine Population (200 Sauen und 10 Eber), die auf Wurfgröße selektiert wurde, unter Berücksichtigung eines Wiederholbarkeitsmodelles und mit zufälligen Herden-Jahr-Saisonklassen simuliert. Anhand der Betriebsdaten wurde die Güte des Modells und die Vorhersagegenauigkeit des Wiederholbarkeitsmodelles mit verschiedenen Definitionen der Herden-Jahr-Saisonklassen geprüft. Mittels der REML-Methode wurden auch Varianzkomponenten für die Herden-Jahr-Saisonklassen geschätzt. Die Herden-Jahr-Saisonklassen wurden als fixer bzw. zufälliger Effekt mit unterschiedlicher Anzahl an Tieren pro Klasse im Modell berücksichtigt. Die Ergebnisse der Betriebsdaten ergaben, dass die Varianz für die Herden-Jahr-Saisonklassen nur einen kleinen Teil der Totalvarianz (von 0,01 bis 0,04) ausmachte. Mit den Herden-Jahr-Saisonklassen als fixer Effekt reduzierte sich die Restvarianz, aber die Größe der Herden-Jahr-Saisonklassen bestimmte nicht allein die Güte des Modells. Die Erhöhung der Vorhersagegenauigkeit ergab keinen signifikanten Anstieg des genetischen Fortschrittes. Abschließend bleibt festzustellen, dass Modelle mit zufälligen Herde-Jahr-Saisonklassen zu einem Bias des geschätzten genetischen Erfolges führten, wenn ein Umwelttrend vorhanden war. [source] Menstrual age,dependent systematic error in sonographic fetal weight estimation: A mathematical modelJOURNAL OF CLINICAL ULTRASOUND, Issue 3 2002Max Mongelli MD Abstract Purpose We used computer modeling techniques to evaluate the accuracy of different types of sonographic formulas for estimating fetal weight across the full range of clinically important menstrual ages. Methods Input data for the computer modeling techniques were derived from published British standards for normal distributions of sonographic biometric growth parameters and their correlation coefficients; these standards had been derived from fetal populations whose ages were determined using sonography. The accuracy of each of 10 formulas for estimating fetal weight was calculated by comparing the weight estimates obtained with these formulas in simulated populations with the weight estimates expected from birth weight data, from 24 weeks' menstrual age to term. Preterm weights were estimated by interpolation from term birth weights using sonographic growth curves. With an ideal formula, the median weight estimates at term should not differ from the population birth weight median. Results The simulated output sonographic values closely matched those of the original population. The accuracy of the fetal weight estimation differed by menstrual age and between various formulas. Most methods tended to overestimate fetal weight at term. Shepard's formula progressively overestimated weights from about 2% at 32 weeks to more than 15% at term. The accuracy of Combs's and Shinozuka's volumetric formulas varied least by menstrual age. Hadlock's formula underestimated preterm fetal weight by up to 7% and overestimated fetal weight at term by up to 5%. Conclusions The accuracy of sonographic fetal weight estimation based on volumetric formulas is more consistent across menstrual ages than are other methods. © 2002 Wiley Periodicals, Inc. J Clin Ultrasound 30:139,144, 2002; DOI 10.1002/jcu.10051 [source] WITHIN-GENERATIONAL AND DIVERSITY-DEPENDENT EFFECTS IN AN INDIVIDUAL-BASED MODEL OF PREDATOR-PREY INTERACTIONNATURAL RESOURCE MODELING, Issue 3 2007W.J. CHIVERS ABSTRACT. In this paper we report the use of an individual-based model of predator-prey interaction to explore the effects of "within generational" and ,between generational' updating of a system level variable. We also report the importance of diversity within the simulated populations. Our findings support those of Grimm and Uchmánski [1994] in regard to the importance of the timing of system level variables, and support Grimm and Uchmañski and others in regard to the importance of the level of diversity across the population. The significance of these findings is emphasized by the fundamental differences between our model and that of Grimm and Uchmánski in regard to the assumptions made about resource flow in the system. This paper was presented at the 2004 Research Modeling Association World Conference on Natural Resource Modeling in Melbourne, Australia. [source] Biases associated with population estimation using molecular taggingANIMAL CONSERVATION, Issue 3 2000Juliann L. Waits Although capture,recapture techniques are often used to estimate population size, these approaches are difficult to implement for a wide variety of species. Highly polymorphic microsatellite markers are useful in individual identification, and these ,molecular tags' can be collected without having to capture or trap the individual. However, several sources of error associated with molecular identification techniques, including failure to identify individuals with the same genotype for these markers as being different, and incorrect assignment of individual genotypes, could bias population estimates. Simulations of populations sampled for the purpose of estimating population size were used to assess the extent of these potential biases. Population estimates tended to be biased downward as the likelihood of individuals sharing the same genotype increased (as measured by the probability of identity (PI) of the multi-locus genotype); this bias increased with population size. Populations of 1000 individuals were underestimated by ,5% when the PI was as small as 1.4 × 10,7. A similar-sized bias did not occur for populations of 50 individuals until the PI had increased to approximately 2.5 × 10,5. Errors in genotype assignment resulted in overestimates of population size; this problem increased with the number of samples and loci that were genotyped. Population estimates were often >200% the size of the simulated populations when the probability of making a genotyping error was 0.05/locus and 7,10 loci were used to identify individuals. This bias was substantially reduced by decreasing genotyping error rate to 0.005. If possible, only highly polymorphic loci that are critical for the identification of the individual should be used in molecular tagging, and considerable efforts should be made to minimize errors in genotype determination. [source] | |||||||||||||