Food selection by a mangrove crab: Temporal changes in fasted animals

The feeding choices of the mangrove crab Ucides cordatus for various mangrove plant leaves (Avicennia schaueriana, Laguncularia racemosa, and Rhizophora mangle) at different ages (mature, senescent pre-abscission, and decomposing leaves) were examined. In a controlled experiment set in a mangrove area, we evaluated crab selection for different plant leaves by analyzing foraging rate (number of leaves with predation marks) and leaf consumption. Crabs were housed individually in plastic containers and after a 3-day fast supplied with leaf fragments every 24 h for 72 h. Uneaten leaves were removed before each new food offering. No food selection was observed in the first day, but after this period, senescent leaves, which have a high polyphenol content, were rejected. On the third day, an interactive effect between plant species and leaf age was shown to affect leaf selection, with mature leaves of A. schaueriana and L. racemosa being more selected than the other treatments. This observation was consistent across crab sexes and ages. Our results show that food selection by this mangrove crab changes through time in fasted animals, suggesting that this variable must be controlled in food preference studies. © 2012 Springer Science+Business Media B.V.

Evaluation of mature cow weight: Genetic correlations with traits used in selection indices, correlated responses, and genetic trends in Nelore cattle

Genetic correlations of selection indices and the traits considered in these indices with mature weight (MW) of Nelore females and correlated responses were estimated to determine whether current selection practices will result in an undesired correlated response in MW. Genetic trends for weaning and yearling indices and MW were also estimated. Data from 612,244 Nelore animals born between 1984 and 2010, belonging to different beef cattle evaluation programs from Brazil and Paraguay, were used. The following traits were studied: weaning conformation (WC), weaning precocity (WP), weaning muscling (WM), yearling conformation (YC), yearling precocity (YP), yearling muscling (YM), weaning and yearling indices, BW gain from birth to weaning (BWG), postweaning BW gain (PWG), scrotal circumference (SC), and MW. The variance and covariance components were estimated by Bayesian inference in a multitrait analysis, including all traits in the same analysis, using a nonlinear (threshold) animal model for visual scores and a linear animal model for the other traits. The mean direct heritabilities were 0.21 ± 0.007 (WC), 0.22 ± 0.007 (WP), 0.20 ± 0.007 (WM), 0.43 ± 0.005 (YC), 0.40 ± 0.005 (YP), 0.40 ± 0.005 (YM), 0.17 ± 0.003 (BWG), 0.21 ± 0.004 (PWG), 0.32 ± 0.001 (SC), and 0.44 ± 0.018 (MW). The genetic correlations between MW and weaning and yearling indices were positive and of medium magnitude (0.30 ± 0.01 and 0.31 ± 0.01, respectively). The genetic changes in weaning index, yearling index, and MW, expressed as units of genetic SD per year, were 0.26, 0.27, and 0.01, respectively. The genetic trend for MW was nonsignificant, suggesting no negative correlated response. The selection practice based on the use of sires with high final index giving preference for those better ranked for yearling precocity and muscling than for conformation generates only a minimal correlated response in MW. © 2013 American Society of Animal Science. All rights reserved.

Analysis of genetic correlations of hip height with selection indices and mature weight in Nelore cattle

Body size is directly related to the productive and reproductive performance of beef cattle raised under free-range conditions. In an attempt to better plan selection criteria, avoiding extremes in body size, this study estimated the heritabilities and genetic correlations of yearling hip height (YH) and mature hip height (MH) with selection indices obtained at weaning (WI) and yearling (YI) and mature weight (MW). Data from 102,373 Nelore animals born between 1984 and 2010, which belong to 263 farms that participate in genetic evaluation programmes of beef cattle conducted in Brazil and Paraguay, were used. The (co)variance components and genetic parameters were estimated by Bayesian inference in multi-trait analysis using an animal model. The mean heritabilities for YH, MH and MW were 0. 56 ± 0. 06, 0. 47 ± 0. 02 and 0. 42 ± 0. 02, respectively. The genetic correlation of YH with WI (0. 13 ± 0. 01) and YI (0. 11 ± 0. 01) was practically zero, whereas a higher correlation was observed with MW (0. 22 ± 0. 03). Positive genetic correlations of medium magnitude were estimated between MH and WI and YI (0. 23 ± 0. 01 and 0. 43 ± 0. 02, respectively). On the other hand, a high genetic correlation (0. 68 ± 0. 03) was observed between the indicator traits of mature body size (MH and MW). Considering the top 20 % of sire (896 sires) in terms of breeding values for the yearling index, the rank sire correlations between breeding values for MH and MW was 0. 62. In general, the results indicate that selection based on WI and YI should not lead to important changes in YH. However, an undesired correlated response in mature cow height is expected, particularly when selection is performed using YI. Therefore, changes in the body structure of Nelore females can be obtained when MH and MW is used as a selection criterion for cows. © 2012 Institute of Plant Genetics, Polish Academy of Sciences, Poznan.

A bayesian analysis for the parameters of the exponential-logarithmic distribution

The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.

Associação e seleção genômica para características relacionadas à eficiência reprodutiva de fêmeas da raça Nelore

Pós-graduação em Zootecnia - FCAV

Genetic Parameters for Reproductive Traits of Crossbred Buffaloes from Brazil, Estimated by Bayesian Inference

The objective of the study was to estimate heritability for calving interval (CI) and age at first calving (AFC) and also calculate repeatability for CI in buffaloes using Bayesian inference. The Brazilian Buffaloes Genetic Improvement Program provided the database. Data consists on information from 628 females and four different herds, born between 1980 and 2003. In order to estimate the variance, univariate analyses were performed employing Gibbs sampler procedure included in the MTGSAM software. The model for CI included the random effects direct additive and permanent environment factors, and the fixed effects of contemporary groups and calving orders. The model for AFC included the direct additive random effect and contemporary groups as a fixed effect. The convergence diagnosis was obtained using Geweke that was implemented through the Bayesian Output Analysis package in R software. The estimated averages were 433.2 days and 36.7months for CI and AFC, respectively. The means, medians and modes for the calculated heritability coefficients were similar. The heritability coefficients were 0.10 and 0.42 for CI and AFC respectively, with a posteriori marginal density that follows a normal distribution for both traits. The repeatability for CI was 0.13. The low heritability estimated for CI indicates that the variation in this trait is, to a large extent, influenced by environmental factors such as herd management policies. The age at first calving has clear potential for yield improvement through direct selection in these animals.

Genetic Correlation for Pre-Weaning and Post-Weaning Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

The aim of this study was to estimate genetic, environmental and phenotypic correlation between birth weight (BW) and weight at 205 days age (W205), BW and weight at 365 days age (W365) and W205-W365, using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data that included 3,883 observations from Mediterranean breed buffaloes. With the purpose to estimate variance and covariance, bivariate analyses were performed using Gibbs sampler that is included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, maternal environmental random effect and contemporary group as fixed effect. The convergence diagnosis was achieved using Geweke, a method that uses an algorithm implemented in R software through the package Bayesian Output Analysis. The calculated direct genetic correlations were 0.34 (BW-W205), 0.25 (BW-W365) and 0.74 (W205-W365). The environmental correlations were 0.12, 0.11 and 0.72 between BW-W205, BW-W365 and W205-W365, respectively. The phenotypic correlations were low for BW-W205 (0.01) and BW-W365 (0.04), differently than the obtained for W205-W365 with a value of 0.67. The results indicate that BW trait have low genetic, environmental and phenotypic association with the two others traits. The genetic correlation between W205 and W365 was high and suggests that the selection for weight at around 205 days could be beneficial to accelerate the genetic gain.

Genetic Parameters for Growth Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

Quantitative analysis of growth genetic parameters is not available for many breeds of buffaloes making selection and breeding decisions an empirical process that lacks robustness. The objective of this study was to estimate heritability for birth weight (BW), weight at 205 days (W205) and 365 days (W365) of age using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data. For the traits BW, W205 and W365 of Brazilian Mediterranean buffaloes 5169, 3792 and 3883 observations have been employed for the analysis, respectively. In order to obtain the estimates of variance, univariate analyses were conducted using the Gibbs sampler included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, random maternal permanent environmental effect and contemporary group that was treated as a fixed effect. The convergence diagnosis was performed employing Geweke, a method that uses an algorithm from the Bayesian Output Analysis package that was implemented using R software environment. The average values for weight traits were 37.6 +/- 4.7 kg for BW, 192.7 +/- 40.3 kg for W205 and 298.6 +/- 67.4 kg for W365. The heritability posterior distributions for direct and maternal effects were symmetric and close to those expected in a normal distribution. Direct heritability estimates obtained using the modes were 0.30 (BW), 0.52 (W205) and 0.54 (W365). The maternal heritability coefficient estimates were 0.31, 0.19 and 0.21 for BW, W205 and W365, respectively. Our data suggests that all growth traits and mainly W205 and W365, have clear potential for yield improvement through direct genetic selection.

Genetic Parameters for Milk Yield and Lactation Length of Crossbred Buffaloes from Brazil by Bayesian Inference

The objective of the study was to estimate heritability and repeatability for milk yield (MY) and lactation length (LL) in buffaloes using Bayesian inference. The Brazilian genetic improvement program of buffalo provided the data that included 628 females, from four herds, born between 1980 and 2003. In order to obtain the estimates of variance, univariate analyses were performed with the Gibbs sampler, using the MTGSAM software. The model for MY and LL included direct genetic additive and permanent environment as random effects, and contemporary groups, milking frequency and calving number as fixed effects. The convergence diagnosis was performed with the Geweke method using an algorithm implemented in R software through the package Bayesian Output Analysis. Average for milk yield and lactation length was 1,546.1 +/- 483.8 kg and 252.3 +/- 42.5 days, respectively. The heritability coefficients were 0.31 (mode), 0.35 (mean) and 0.34 (median) for MY and 0.11 (mode), 0.10 (mean) and 0.10 (median) for LL. The repeatability coefficient (mode) were 0.50 and 0.15 for MY and LL, respectively. Milk yield is the only trait with clear potential for genetic improvement by direct genetic selection. The repeatability for MY indicates that selection based on the first lactation could contribute for an improvement in this trait.

Genetic Parameters for Productive and Reproductive Traits for Milk Buffalo in Brazil

Knowing the genetic parameters of productive and reproductive traits in milking buffaloes is essential for planning and implementing of a program genetic selection. In Brazil, this information is still scarce. The objective of this study was to verify the existence of genetic variability in milk yield of buffaloes and their constituents, and reproductive traits for the possibility of application of the selection. A total of 9,318 lactations records from 3,061 cows were used to estimate heritabilities for milk yield (MY), fat percentage (%F), protein percentage (%P), length of lactation (LL), age of first calving (AFC) and calving interval (CI) and the genetic correlations among traits MY, %F and %P. The (co) variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co) variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. Estimated heritability values for MY, % F, % P, LL, AFC and CI were 0.24, 0.34, 0.40, 0.09, 0.16 and 0.05, respectively. The genetic correlation estimates among MY and % F, MY and % P and % F and % P were -0.29, -0.18 and 0.25, respectively. The production of milk and its constituents showed enough genetic variation to respond to a selection program. Negative estimates of genetic correlations between milk production and its components suggest that selection entails a reduction in the other.

Filippov's selection theorem and the existence of solutions for optimal control problems in time scales

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multiple-trait genomic evaluation for milk yield and milk quality traits using genomic and phenotypic data in buffalo in Brazil

The use of markers distributed all long the genome may increase the accuracy of the predicted additive genetic value of young animals that are candidates to be selected as reproducers. In commercial herds, due to the cost of genotyping, only some animals are genotyped and procedures, divided in two or three steps, are done in order to include these genomic data in genetic evaluation. However, genomic evaluation may be calculated using one unified step that combines phenotypic data, pedigree and genomics. The aim of the study was to compare a multiple-trait model using only pedigree information with another using pedigree and genomic data. In this study, 9,318 lactations from 3061 buffaloes were used, 384 buffaloes were genotyped using a Illumina bovine chip (Illumina Infinium (R) bovineHD BeadChip). Seven traits were analyzed milk yield (MY), fat yield (FY), protein yield (PY), lactose yield (LY), fat percentage (F%), protein percentage (P%) and somatic cell score (SCSt). Two analyses were done: one using phenotypic and pedigree information (matrix A) and in the other using a matrix based in pedigree and genomic information (one step, matrix H). The (co) variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year-calving season), number of milking (2 levels), and age of buffalo at calving as (co) variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The heritability estimates using matrix A were 0.25, 0.22, 0.26, 0.17, 0.37, 0.42 and 0.26 and using matrix H were 0.25, 0.24, 0.26, 0.18, 0.38, 0.46 and 0.26 for MY, FY, PY, LY, % F, % P and SCCt, respectively. The estimates of the additive genetic effect for the traits were similar in both analyses, but the accuracy were bigger using matrix H (superior to 15% for traits studied). The heritability estimates were moderated indicating genetic gain under selection. The use of genomic information in the analyses increases the accuracy. It permits a better estimation of the additive genetic value of the animals.

The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Seleção genômica para características de carcaça em bovinos da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Multicollinearity and financial constraint in investment decisions: a bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.