Trait contributions to fish community assembly emerge from trophic interactions in an individual-based model

Community ecology seeks to understand and predict the characteristics of communities that can develop under different environmental conditions, but most theory has been built on analytical models that are limited in the diversity of species traits that can be considered simultaneously. We address that limitation with an individual-based model to simulate assembly of fish communities characterized by life history and trophic interactions with multiple physiological tradeoffs as constraints on species performance. Simulation experiments were carried out to evaluate the distribution of 6 life history and 4 feeding traits along gradients of resource productivity and prey accessibility. These experiments revealed that traits differ greatly in importance for species sorting along the gradients. Body growth rate emerged as a key factor distinguishing community types and defining patterns of community stability and coexistence, followed by egg size and maximum body size. Dominance by fast-growing, relatively large, and fecund species occurred more frequently in cases where functional responses were saturated (i.e. high productivity and/or prey accessibility). Such dominance was associated with large biomass fluctuations and priority effects, which prevented richness from increasing with productivity and may have limited selection on secondary traits, such as spawning strategies and relative size at maturation. Our results illustrate that the distribution of species traits and the consequences for community dynamics are intimately linked and strictly dependent on how the benefits and costs of these traits are balanced across different conditions. © 2012 Elsevier B.V.

Rubber tree early selection for yield stability in time and among locations

Rubber production in the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] can be expressed differently in different environments. Thus the objective of the present study was to select productive progenies, stable and responsive in time and among locations. Thirty progenies were assessed by early yield tests at three ages and in three locations. A randomized block design was used with three replications and ten plants per plot, in 3 × 3 m spacing. The procedure of the mixed linear Reml/Blup model-restricted maximum likelihood/best non-biased linear prediction was used in the genetic statistical analyses. In all the individual analyses, the values observed for the progeny average heritability (ĥpa 2) were greater than those of the additive effect based on single individuals (ĥa 2) and within plot additive (ĥad 2). In the joint analyses in time, there was genotype × test interaction in the three locations. When 20 % of the best progenies were selected the predicted genetic gains were: Colina GG = 24.63 %, Selvíria GG = 13.63 %, and Votuporanga GG = 25.39 %. Two progenies were among the best in the analyses in the time and between locations. In the joint analysis among locations there was only genotype × location interaction in the first early test. In this test, selecting 20 %, the general predicted genetic gain was GG = 25.10 %. Identifying progenies with high and stable yield over time and among locations contributes to the efficiency of the genetic breeding program. The relative performance of the progenies varies depending of the age of early selection test. © 2013 Springer Science+Business Media Dordrecht.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Genetic parameters and simultaneous selection for root yield, adaptability and stability of cassava genotypes

O objetivo deste trabalho foi estimar parâmetros genéticos e avaliar a seleção simultânea quanto à produtividade de raízes e à adaptabilidade e estabilidade de genótipos de mandioca. Os efeitos dos genótipos foram considerados como fixos e aleatórios, e a metodologia de modelos mistos (REML/Blup) foi utilizada para estimar os parâmetros genéticos e a média harmônica do desempenho relativo dos valores genotípicos (MHPRVG), para seleção simultânea. Dez genótipos foram avaliados em delineamento de blocos ao acaso, com quatro repetições. O experimento foi realizado nos municípios de Altamira, Santarém e Santa Luzia do Pará, PA, nos anos agrícolas de 2009/2010, 2010/2011 e 2011/2012. As raízes foram colhidas 12 meses após o plantio, em todos os locais testados. A produtividade de raízes apresentou baixo coeficiente de variação genotípica (4,25%) e herdabilidade de parcelas individuais no sentido amplo (0,0424), o que resultou em baixo ganho genético. Em razão da baixa correlação genotípica (0,15), a classificação dos genótipos quanto à produtividade de raízes variou de acordo com o ambiente. Os genótipos CPATU 060, CPATU 229 e CPATU 404 destacaram-se quanto à produtividade, adaptabilidade e estabilidade.

A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

A note on model uncertainty in linear regression

We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

Site Selection for Restoration Planning: A Protocol With Landscape and Legislation Based Alternatives

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate analysis for animal selection in experimental research

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reaction norm model to describe environmental sensitivity across first lactation in dairy cattle under tropical conditions

Reaction norm models have been widely used to study genotype by environment interaction (G × E) in animal breeding. The objective of this study was to describe environmental sensitivity across first lactation in Brazilian Holstein cows using a reaction norm approach. A total of 50,168 individual monthly test day (TD) milk yields (10 test days) from 7476 complete first lactations of Holstein cattle were analyzed. The statistical models for all traits (10 TDs and for 305-day milk yield) included the fixed effects of contemporary group, age of cow (linear and quadratic effects), and days in milk (linear effect), except for 305-day milk yield. A hierarchical reaction norm model (HRNM) based on the unknown covariate was used. The present study showed the presence of G × E in milk yield across first lactation of Holstein cows. The variation in the heritability estimates implies differences in the response to selection depending on the environment where the animals of this population are evaluated. In the average environment, the heritabilities for all traits were rather similar, in range from 0.02 to 0.63. The scaling effect of G × E predominated throughout most of lactation. Particularly during the first 2 months of lactation, G × E caused reranking of breeding values. It is therefore important to include the environmental sensitivity of animals according to the phase of lactation in the genetic evaluations of Holstein cattle in tropical environments.

Body condition score of Nellore beef cows: a heritable measure to improve the selection of reproductive and maternal traits

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The briefing in paleodesign: selection and arrangement of data for the reconstruction of Paleovertebrates

Representing visually the external appearance of an extinct animal requires, for a reasonably reliable and expressive reconstitution, a good compilation and arrangement of the scientific conclusions on the fossil findings. It is proposed in this work an initial model of a briefing to be applied in a paleodesign process of a paleovertebrate. Briefing can be understood as a gathering of all necessary data to perform a project. We point out what must be known about the relevant structures in order to access all the data and the importance of such information. It is expected that the present briefing suggested might be faced with flexibility, serving as a facilitating interface of the relation between paleoartists and paleontologists.

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.

A matheuristic for the selection of beam directions and dose distribution in radiotherapy planning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.