Seasonal variation in leaf traits between congeneric savanna and forest trees in Central Brazil: Implications for forest expansion into savanna

The ecology of forest and savanna trees species will largely determine the structure and dynamics of the forest-savanna boundaries, but little is known about the constraints to leaf trait variation imposed by selective forces and evolutionary history during the process of savanna invasion by forest species. We compared seasonal patterns in leaf traits related to leaf structure, carbon assimilation, water, and nutrient relations in 10 congeneric species pairs, each containing one savanna species and one forest species. All individuals were growing in dystrophic oxisols in a fire-protected savanna of Central Brazil. We tested the hypothesis that forest species would be more constrained by seasonal drought and nutrient-poor soils than their savanna congeners. We also hypothesized that habitat, rather than phylogeny, would explain more of the interspecific variance in leaf traits of the studied species. We found that throughout the year forest trees had higher specific leaf area (SLA) but lower integrated water use efficiency than savanna trees. Forest and savanna species maintained similar values of predawn and midday leaf water potential along the year. Lower values were measured in the dry season. However, this was achieved by a stronger regulation of stomatal conductance and of CO2 assimilation on an area basis (A area) in forest trees, particularly toward the end of the dry season. Relative to savanna trees, forest trees maintained similar (P, K, Ca, and Mg) or slightly higher (N) leaf nutrient concentrations. For the majority of traits, more variance was explained by phylogeny, than by habitat of origin, with the exception of SLA, leaf N concentration, and A area, which were apparently subjected to different selective pressures in the savanna and forest environments. In conclusion, water shortage during extended droughts would be more limiting for forest trees than nutrient-poor soils. © 2013 Springer-Verlag Berlin Heidelberg.

Growth and carbon isotopes of Mediterranean trees reveal contrasting responses to increased carbon dioxide and drought

Forest dynamics will depend upon the physiological performance of individual tree species under more stressful conditions caused by climate change. In order to compare the idiosyncratic responses of Mediterranean tree species (Quercus faginea, Pinus nigra, Juniperus thurifera) coexisting in forests of central Spain, we evaluated the temporal changes in secondary growth (basal area increment; BAI) and intrinsic water-use efficiency (iWUE) during the last four decades, determined how coexisting species are responding to increases in atmospheric CO2 concentrations (Ca) and drought stress, and assessed the relationship among iWUE and growth during climatically contrasting years. All species increased their iWUE (ca. +15 to +21 %) between the 1970s and the 2000s. This increase was positively related to Ca for J. thurifera and to higher Ca and drought for Q. faginea and P. nigra. During climatically favourable years the study species either increased or maintained their growth at rising iWUE, suggesting a higher CO2 uptake. However, during unfavourable climatic years Q. faginea and especially P. nigra showed sharp declines in growth at enhanced iWUE, likely caused by a reduced stomatal conductance to save water under stressful dry conditions. In contrast, J. thurifera showed enhanced growth also during unfavourable years at increased iWUE, denoting a beneficial effect of Ca even under climatically harsh conditions. Our results reveal significant inter-specific differences in growth driven by alternative physiological responses to increasing drought stress. Thus, forest composition in the Mediterranean region might be altered due to contrasting capacities of coexisting tree species to withstand increasingly stressful conditions. © 2013 Springer-Verlag Berlin Heidelberg.

Simulated annealing for building roof contours identification from lidar data

This paper proposes a method by simulated annealing for building roof contours identification from LiDAR-derived digital elevation model. Our method is based on the concept of first extracting aboveground objects and then identifying those objects that are building roof contours. First, to detect aboveground objects (buildings, trees, etc.), the digital elevation model is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing algorithm. Experiments carried out with laser scanning digital elevation model showed that the methodology works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Influence of potassium and sodium nutrition on leaf area components in Eucalyptus grandis trees

Background and Aims: Recent studies showed a positive tree response to Na addition in K-depleted tropical soils. Our study aimed to gain insight into the effects of K and Na fertilizations on leaf area components for a widely planted tree species. Methods: Leaf expansion rates, as well as nutrient, polyol and soluble sugar concentrations, were measured from emergence to abscission of tagged leaves in 1-year-old Eucalyptus grandis plantations. Leaf cell size and water status parameters were compared 1 and 2 months after leaf emergence in plots with KCl application (+K), NaCl application (+Na) and control plots (C). Results: K and Na applications enhanced tree leaf area by increasing both leaf longevity and the mean area of individual leaves. Higher cell turgor in treatments +K and +Na than in the C treatment resulting from higher concentrations of osmotica contributed to increasing both palisade cell diameters and the size of fully expanded leaves. Conclusions: Intermediate total tree leaf area in treatment +Na compared to treatments C and +K might result from the capacity of Na to substitute K in osmoregulatory functions, whereas it seemed unable to accomplish other important K functions that contribute to delaying leaf senescence. © 2013 Springer Science+Business Media Dordrecht.

Qualidade do sombreamento natural de três espécies arbóreas visando ao conforto térmico animal

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparação entre três critérios de amostragem para a avaliação da densidade básica da madeira de florestas implantadas de eucaliptos

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Alterações genômicas em tumores de glândulas salivares

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Variação genética, herdabilidades e ganhos na seleção para caracteres de crescimento e forma, em teste de progênies de polinização aberta de Eucalyptus cloeziana, aos 24 anos de idade em Luiz Antônio-SP

Pós-graduação em Agronomia - FEIS

Extração automática de contornos de telhados de edifícios em um modelo digital de elevação, utilizando inferência Bayesiana e campos aleatórios de Markov

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise filogenética das espécies de Hemigrammus Gill, 1858 (Characiformes, Characidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise da composição e estrutura da comunidade arbustivo-arbórea de savana no sudoeste de Mato Grosso-Brasil

Pós-graduação em Ciências Biológicas (Biologia Vegetal) - IBRC

Estudo de algoritmos para classificação de séries temporais: uma aplicação em qualidade de energia elétrica

Aborda a classificação automática de faltas do tipo curto-circuito em linhas de transmissão. A maioria dos sistemas de transmissão possuem três fases (A, B e C). Por exemplo, um curto-circuito entre as fases A e B pode ser identicado como uma falta\AB". Considerando a possibilidade de um curto-circuito com a fase terra (T), a tarefa ao longo desse trabalho de classificar uma série temporal em uma das 11 faltas possíveis: AT, BT, CT, AB, AC, BC, ABC, ABT, ACT, BCT, ABCT. Estas faltas são responsáveis pela maioria dos distúrbios no sistema elétrico. Cada curto-circuito é representado por uma seqüência (série temporal) e ambos os tipos de classificação, on-line (para cada curto segmento extraído do sinal) e off-line (leva em consideração toda a seqüência), são investigados. Para evitar a atual falta de dados rotulados, o simulador Alternative Transient Program (ATP) é usado para criar uma base de dados rotulada e disponibilizada em domínio público. Alguns trabalhos na literatura não fazem distinção entre as faltas ABC e ABCT. Assim, resultados distinguindo esse dois tipos de faltas adotando técnicas de pré-processamento, diferentes front ends (por exemplo wavelets) e algoritmos de aprendizado (árvores de decisão e redes neurais) são apresentados. O custo computacional estimado durante o estágio de teste de alguns classificadores é investigado e a escolha dos parâmetros dos classificadores é feita a partir de uma seleção automática de modelo. Os resultados obtidos indicam que as árvores de decisão e as redes neurais apresentam melhores resultados quando comparados aos outros classificadores.