Changes in phytoplankton composition in response to tides, wind-induced mixing conditions, and freshwater outflows in an urbanised estuarine complex

Estudos recentes na baía de Santos (sudeste do Brasil), localizada em um sistema estuarino altamente urbanizado, mostraram o aumento de espécies fitoplanctônicas potencialmente nocivas. Apesar da importância da previsão das florações algais nocivas, é difícil determinar a estrutura da comunidade fitoplanctônica em ambientes extremamente dinâmicos. O presente estudo analisa florações dominadas pelo microfitoplâncton e sua relação com variáveis físicas e meteorológicas, a fim de determinar padrões associados às marés e às estações do ano. Foram comparadas oito situações e obtidos cinco cenários de dominância relacionados aos ventos, marés e pluviosidade: i) Surfers, diatomáceas associadas à zona de surfe, de alta energia; ii) Sinkers, diatomáceas de tamanho grande que ocorrem nas marés de sizígia, depois de períodos de alta pluviosidade; iii) Opportunistic mixers, diatomáceas pequenas ou alongadas, formadoras de cadeia, que ocorrem durante períodos de quadratura; iv) Local mixers, diatomáceas e dinoflagelados microplanctônicos que foram abundantes em todas as 298 estações amostradas, e v) Mixotrophic dinoflagellates, que ocorrem após intensas descargas estuarinas. Os resultados sugerem uma alteração no padrão temporal de algumas espécies formadoras de florações, enquanto outras apresentaram abundâncias superiores aos valores seguros para a saúde publica. Esta abordagem ilustra também os possíveis impactos de variações na descarga de água doce em estuários altamente eutrofizados.

Mineral stress response of Vitis cell wall transcriptome: identification, characterization and expression of genes from key families involved in wall biosynthesis and modification

Doutoramento em Engenharia Agronómica - Instituto Superior de Agronomia - UL

Assessing Decision Making Skill in Complex and Dynamic Environments Using Representative and Simulated Tasks

Traditional decision making research has often focused on one's ability to choose from a set of prefixed options, ignoring the process by which decision makers generate courses of action (i.e., options) in-situ (Klein, 1993). In complex and dynamic domains, this option generation process is particularly critical to understanding how successful decisions are made (Zsambok & Klein, 1997). When generating response options for oneself to pursue (i.e., during the intervention-phase of decision making) previous research has supported quick and intuitive heuristics, such as the Take-The-First heuristic (TTF; Johnson & Raab, 2003). When generating predictive options for others in the environment (i.e., during the assessment-phase of decision making), previous research has supported the situational-model-building process described by Long Term Working Memory theory (LTWM; see Ward, Ericsson, & Williams, 2013). In the first three experiments, the claims of TTF and LTWM are tested during assessment- and intervention-phase tasks in soccer. To test what other environmental constraints may dictate the use of these cognitive mechanisms, the claims of these models are also tested in the presence and absence of time pressure. In addition to understanding the option generation process, it is important that researchers in complex and dynamic domains also develop tools that can be used by `real-world' professionals. For this reason, three more experiments were conducted to evaluate the effectiveness of a new online assessment of perceptual-cognitive skill in soccer. This test differentiated between skill groups and predicted performance on a previously established test and predicted option generation behavior. The test also outperformed domain-general cognitive tests, but not a domain-specific knowledge test when predicting skill group membership. Implications for theory and training, and future directions for the development of applied tools are discussed.

Using an infra-red sensor to measure the dynamic behaviour of N2O gas escaping through different sized holes

An anastomosis is a surgical procedure that consists of the re-connection of two parts of an organ and is commonly required in cases of colorectal cancer. Approximately 80% of the patients diagnosed with this problem require surgery. The malignant tissue located on the gastrointestinal track must be resected and the most common procedure adopted is the anastomosis. Studies made with 2,980 patients that had this procedure, show that the leakage through the anastomosis was 5.1%. This paper discusses the dynamic behavior of N2O gas through different sized leakages as detected by an Infra-Red gas sensor and how the sensors response time changes depending on the leakage size. Different sized holes were made in the rigid tube to simulate an anastomostic leakage. N2O gas was injected into the tube through a pipe and the leakage rate measured by the infra-red gas sensor. Tests were also made experimentally also using a CFD (Computational Fluid Dynamics) package called FloWorks. The results will be compared and discussed in this paper.

Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw

Over the past decades, vegetation and climate have changed significantly in the Arctic. Deciduous shrub cover is often assumed to expand in tundra landscapes, but more frequent abrupt permafrost thaw resulting in formation of thaw ponds could lead to vegetation shifts towards graminoid-dominated wetland. Which factors drive vegetation changes in the tundra ecosystem are still not sufficiently clear. In this study, the dynamic tundra vegetation model, NUCOM-tundra (NUtrient and COMpetition), was used to evaluate the consequences of climate change scenarios of warming and increasing precipitation for future tundra vegetation change. The model includes three plant functional types (moss, graminoids and shrubs), carbon and nitrogen cycling, water and permafrost dynamics and a simple thaw pond module. Climate scenario simulations were performed for 16 combinations of temperature and precipitation increases in five vegetation types representing a gradient from dry shrub-dominated to moist mixed and wet graminoid-dominated sites. Vegetation composition dynamics in currently mixed vegetation sites were dependent on both temperature and precipitation changes, with warming favouring shrub dominance and increased precipitation favouring graminoid abundance. Climate change simulations based on greenhouse gas emission scenarios in which temperature and precipitation increases were combined showed increases in biomass of both graminoids and shrubs, with graminoids increasing in abundance. The simulations suggest that shrub growth can be limited by very wet soil conditions and low nutrient supply, whereas graminoids have the advantage of being able to grow in a wide range of soil moisture conditions and have access to nutrients in deeper soil layers. Abrupt permafrost thaw initiating thaw pond formation led to complete domination of graminoids. However, due to increased drainage, shrubs could profit from such changes in adjacent areas. Both climate and thaw pond formation simulations suggest that a wetter tundra can be responsible for local shrub decline instead of shrub expansion.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian Semiarid region, for use in Dynamic Global Vegetation Models.

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga

Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2.

Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO2, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO2 concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcmax), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcmax is directly related to photosynthesis acclimation (loss of response to elevated CO2), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO2 and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation?atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.