264 research outputs found

    Pedigree analysis applied to an endangered buffalo population: possible management strategy.

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    The Carabao breed (Bubalus bubalis kerebao) in Brazil may be endangered and at risk of losing specific qualities. This makes preservation and population studies extremely important. In this pedigree analysis on a Brazilian herd, low values for populational parameters and high mean endogamy were found. Mating optimization based on bulls of lesser kinship improves populational parameters and reduces inbreeding rates. Use of this tool, in addition to conservation programs, will help to mitigate genetic variability losses in the Brazilian Carabao herd, thus allowing its future enrollment in genetic improvement programs

    Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest.

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    Land surface models diverge in their predictions of the Amazon forest's response to climate change-induced droughts, with some showing a catastrophic collapse of forests, while others simulating resilience. Therefore, observations of tropical ecosystem responses to real-world droughts and other extreme events are needed. We report long-term seasonal dynamics of photosynthesis, respiration, net carbon exchange, phenology, and tree demography and characterize the effect of dry and wet events on ecosystem form and function at the Tapajós National Forest, Brazil, using over two decades of eddy covariance observations that include the 2015–2016 El Niño drought and La Niña 2008–2009 wet periods. We found strong forest responses to both ENSO events: La Niña saw forest net carbon loss from reduced photosynthesis (due to lower incoming radiation from increased cloudiness) even as ecosystem respiration (Reco) was maintained at mean seasonal levels. El Niño induced the opposite short-term effect, net carbon gains, despite significant reductions in photosynthesis (from a drought-induced halving of canopy conductance to CO2 and significant losses of leaf area), because drought suppression of Reco losses was even greater. However, long-term responses to the two climate perturbations were very different: transient during La Niña –the forest returned to its “normal” state as soon as the climate did, and long-lasting during El Niño –leaf area loss and associated declines in photosynthetic capacity (Pc) and canopy conductance were exacerbated and extended by feedbacks from higher temperatures and atmospheric evaporative demand and persisted for ∼3+ years after normal rainfall resumed. These findings indicate that these forests are more vulnerable to drought than to excess rain, because drought drives significant changes in forest structure (e.g., leaf-abscission and mortality) and ecosystem function (e.g. reduced stomatal conductance). As future Amazonian climate change increases frequencies of hydrological extremes, these mechanisms will determine the long-term fate of tropical forests

    Optimizing mate selection: a genetic algorithm approach.

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    Background: Genetic Improvement Programs (GIP) aim to enhance productionefficiency of beef cattle. The main way to guide this enhancement is by choosing thebest mates among sires and cows, in order to maximize the offspring GeneticQualification Index (QGI), which is measured by an index defined by the GIP andcomputed for each animal of the herd. This paper describes a genetic algorithm, whichcan recommend an optimal set of matings among sires and cows, in order to maximizethe QGI of the herd. Breeders can define constraints regarding level of problems,which must be avoided, and they also can alter the traits relative importance consideredin QGI, according their particular interests. This algorithm was applied to a herd of aBrazilian breeder, which participates of a GIP, and it found optimal matings in order toincrease QGI value. We have simulated different scenarios considering variations onfitness functions, which combine QGI and level of problems, in order to find the optimalmatings. Proposed approach was successfully used to recommend optimal matingdecisions by Brazilian Hereford and Braford cattle breeders Association leading to animprovement of offspring QGI.Keywords: Genetic Improvement, Beef Cattle, Artificial Intelligence, EvolutionaryComputing.Editors: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke

    Variations in Amazonian forest canopy structure and light environments across environmental and disturbance gradients.

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    A critical problem in tropical forest ecology is understanding how vegetation structure and function vary over environmental gradients. The degree to which forest structure changes across the Amazon basin and the role of environmental variability in shaping forest structure and dynamics are poorly characterised, despite the importance of these forests for regional and global climate. To address these challenges, we connected 10 years of investigations to amass a large database of ground-based profiling canopy lidar (PCL) data from 297 Amazon forest plots across large-scale environmental and disturbance gradients. Mean annual precipitation varied from 1,963 to 3,159 mm, number of dry season months from 0 to 5, and plot soil types covered about half of the variation in phosphorus, exchangeable cation, and soil physical property values observed in Amazonia. We quantified detailed metrics of vertical and horizontal structure and canopy light environments. Forest structure varied considerably across plots; maximum canopy height ranged from 6.1 to 35.7 m, gap fraction from 0.00 to 0.36, LAI from 0.5 to 7.3, rugosity from 1.5 to 7.5 m, and the relative height of 50% light transmission from 0.3 to 0.8. Disturbed sites exhibited almost twice the level of variation (SD) to non-disturbed sites for many metrics. Vertical leaf area density (LAD) profiles also showed high between plot variability, especially at low and high relative canopy heights. Plots with similar LAD profiles sometimes exhibited different distributions of ?canopy photic environment layers??where canopy leaf area is separated into photic environment layers by depth from canopy surface. This demonstrates that LAD profiles alone are insufficient for characterising canopy environments, essential to light-driven regeneration and carbon cycle processes. In addition, we evaluated relationships between lidar metrics and environmental variables extracted from geospatial layers. Our dataset allows a unique and detailed multi-site analysis of canopy structure and environments across the Amazon, including regions with little or no lidar sampling. Examining how structural attributes alter across environmental gradients is critical to understanding how current and future climate influences Amazonian forest structure, function, and dynamics.Paper 499657
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