GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Parameterisation, evaluation and comparison of pesticide leaching models to data from a Bologna field site, Italy

Effective prediction of pesticide fate using mathematical models requires good process descriptions in the models and good choice of parameter values by the user. This paper examines the ability of seven pesticide leaching models (LEACHP, MACRO, PELMO, PESTLA, PLM, PRZM and VARLEACH) to describe an arable field environment where sunflowers are grown in the Po Valley, northern Italy. Two pesticides were considered, aclonifen and ethoprophos. The models were evaluated in terms of their ability to reproduce field data of soil water content and pesticide residues in the soil and ground water. The evaluation was based on a combination of calibrated and uncalibrated runs. The results from the models were compared with each other to explore the differences between the models. The models varied in their ability to predict soil water content in the summer: the capacity models PRZM, PELMO and VARLEACH predicted less drying than MACRO, PESTLA, PLM and LEACHP. The models varied in their ability to simulate the persistence of the pesticides in the soil. Differences in the simulated pesticide degradation rate were observed between the models, due to variations in the simulated soil water content and soil temperature, and also differences in the equation linking degradation rate to soil water content. There were large differences among the predictions of the models for the mean leaching depth of ethoprophos. PRZM, PELMO, PESTLA and LEACHP all showed similar mean leaching depth to each other, whereas VARLEACH predicted lower ethoprophos mobility and PLM and MACRO predicted greater mobility. All the models overpredicted dispersion of ethoprophos through the soil profile, as compared to the field data. None of the models was able to simulate the field data of rapid leaching of pesticide to ground water except PLM after calibration of the percentage of macropores in the mobile pore space. More work is required in the parameterisation of macropore flow for those models that include this process. (C) 2002 Society of Chemical Industry

Modeling Plant Tissue Growth and Cell Division

Morphogenesis is the creation of form, a complex process requiring the integration of genetics, mechanics, and geometry. Patterning processes driven by molecular regulatory and signaling networks interact with growth to create organ shape, often in unintuitive ways. Computer simulation modeling is becoming an increasingly important tool to aid our understanding of these complex interactions. In this chapter we introduce computational approaches for studying these processes on spatial, multicellular domains. For some problems, such as the exploration of many patterning processes, simulation can be done on static (non-growing) templates. These can range from abstract idealized cells, such as rectangular or hex grids, to more realistic shapes such as Voronoi regions, or even shapes extracted from bio-imaging data. More dynamic processes like phyllotaxis involve the interaction of growth and patterning, and require the simulation of growing domains. In the simplest case growth can be modeled descriptively, provided as an input to the model. Growth is specified globally, and must be designed carefully to avoid conflicts (growing cells must fit together). We present several methods for this that can be applied to shoots, roots, leaves, and other plant organs. However when shape is an emergent property of the model, different cells or areas of the tissue need to specify their growth locally, and physically-based methods (mechanics) are required to resolve conflicts. Among these are mass-spring, finite element, and Hamiltonian-based approaches

Impact of climate change and loss of habitat on sirenians

Although the impacts of climate change on the welfare of individual manatees and dugongs are still uncertain, the effects are likely to be through indirect interactions between meteorological and biotic factors and the human responses to climate change. We divided the potential impacts into (1) those that will potentially affect sirenians directly including temperature increases, sea-level rise, increased intensity of extreme weather events and changes in rainfall patterns and (2) indirect impacts that are likely to cause harm through habitat loss and change and the increase in the likelihood of harmful algal blooms and disease outbreaks. The habitat modification accompanying sea-level rise is likely to decrease the welfare of sirenians including increased mortality. Many species of tropical seagrasses live close to their thermal limits and will have to up-regulate their stress-response systems to tolerate the sublethal temperature increases caused by climate change. The capacity of seagrass species to evoke such responses is uncertain, as are the effects of elevated carbon dioxide on such acclimation responses. The increase in the intensity of extreme weather events associated with climate change is likely to decrease the welfare of sirenians through increased mortality from strandings, as well as habitat loss and change. These effects are likely to increase the exposure of sirenians to disease and their vulnerability to predators, including human hunters. Climate-related hazards will also exacerbate other stressors, especially for people living in poverty. Thus the risks to sirenians from climate change are likely to be greatest for small populations of dugongs and manatees occurring in low-income countries. The African manatee will be particularly vulnerable because of the high levels of human poverty throughout most of its range resulting in competition for resources, including protein from manatee meat