891 resultados para large spatial scale
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About 1,200 ha of hydrilla ( Hydrilla verticillata L.f. Royle) was eliminated in the Spring Creek embayment of Lake Seminole, Georgia, using a drip-delivery application of fluridone (1- methyl-3-phenyl-5-[3-(trifluoromethl) phenyl]-4(1H)-pyridinone) in 2000 and 2001. Two groups of 15 and 20 largemouth bass (Micropterus salmoides Lacepede) were implanted with 400-day radio tags in February 2000 and 2001 to determine changes in movement and behavior before and after hydrilla reduction.(PDF contains 8 pages.)
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An overview of the workflow process the MBLWHOI Library has created through their digitization efforts with the Internet Archive as the part of two consortial projects. This includes some lessons learned as well as future plans to facilitate access. (21 powerpoint slides)
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Since 1999, NOAA’s Biogeography Branch of the Center for Coastal Monitoring and Assessment (CCMA-BB) has been working with federal and territorial partners to characterize, monitor, and assess the status of the marine environment around northeastern St. Croix, U.S. Virgin Islands. This effort is part of the broader NOAA Coral Reef Conservation Program’s (CRCP) National Coral Reef Ecosystem Monitoring Program (NCREMP). With support from CRCP’s NCREMP, CCMA conducts the “Caribbean Coral Reef Ecosystem Monitoring project” (CREM) with goals to: (1) spatially characterize and monitor the distribution, abundance, and size of marine fauna associated with shallow water coral reef seascapes (mosaics of coral reefs, seagrasses, sand and mangroves); (2) relate this information to in situ fine-scale habitat data and the spatial distribution and diversity of habitat types using benthic habitat maps; (3) use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting; (4) establish the efficacy of those management decisions; and (5) develop data collection and data management protocols. The monitoring effort in northeastern St. Croix was conducted through partnerships with the National Park Service (NPS) and the Virgin Islands Department of Planning and Natural Resources (VI-DPNR). The geographical focal point of the research is Buck Island Reef National Monument (BIRNM), a protected area originally established in 1961 and greatly expanded in 2001; however, the work also encompassed a large portion of the recently created St. Croix East End Marine Park (EEMP). Project funding is primarily provided by NOAA CRCP, CCMA and NPS. In recent decades, scientific and non-scientific observations have indicated that the structure and function of the coral reef ecosystem around northeastern St. Croix have been adversely impacted by a wide range of environmental stressors. The major stressors have included the mass Diadema die off in the early 1980s, a series of hurricanes beginning with Hurricane Hugo in 1989, overfishing, mass mortality of Acropora corals due to disease and several coral bleaching events, with the most severe mass bleaching episode in 2005. The area is also an important recreational resource supporting boating, snorkeling, diving and other water based activities. With so many potential threats to the marine ecosystem and a dramatic change in management strategy in 2003 when the park’s Interim Regulations (Presidential Proclamation No. 7392) established BIRNM as one of the first fully protected marine areas in NPS system, it became critical to identify existing marine fauna and their spatial distributions and temporal dynamics. This provides ecologically meaningful data to assess ecosystem condition, support decision making in spatial planning (including the evaluation of efficacy of current management strategies) and determine future information needs. The ultimate goal of the work is to better understand the coral reef ecosystems and to provide information toward protecting and enhancing coral reef ecosystems for the benefit of the system itself and to sustain the many goods and services that it offers society. This Technical Memorandum contains analysis of the first six years of fish survey data (2001-2006) and associated characterization of the benthos (1999-2006). The primary objectives were to quantify changes in fish species and assemblage diversity, abundance, biomass and size structure and to provide spatially explicit information on the distribution of key species or groups of species and to compare community structure inside (protected) versus outside (fished) areas of BIRNM. (PDF contains 100 pages).
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The mapping and geospatial analysis of benthic environments are multidisciplinary tasks that have become more accessible in recent years because of advances in technology and cost reductions in survey systems. The complex relationships that exist among physical, biological, and chemical seafloor components require advanced, integrated analysis techniques to enable scientists and others to visualize patterns and, in so doing, allow inferences to be made about benthic processes. Effective mapping, analysis, and visualization of marine habitats are particularly important because the subtidal seafloor environment is not readily viewed directly by eye. Research in benthic environments relies heavily, therefore, on remote sensing techniques to collect effective data. Because many benthic scientists are not mapping professionals, they may not adequately consider the links between data collection, data analysis, and data visualization. Projects often start with clear goals, but may be hampered by the technical details and skills required for maintaining data quality through the entire process from collection through analysis and presentation. The lack of technical understanding of the entire data handling process can represent a significant impediment to success. While many benthic mapping efforts have detailed their methodology as it relates to the overall scientific goals of a project, only a few published papers and reports focus on the analysis and visualization components (Paton et al. 1997, Weihe et al. 1999, Basu and Saxena 1999, Bruce et al. 1997). In particular, the benthic mapping literature often briefly describes data collection and analysis methods, but fails to provide sufficiently detailed explanation of particular analysis techniques or display methodologies so that others can employ them. In general, such techniques are in large part guided by the data acquisition methods, which can include both aerial and water-based remote sensing methods to map the seafloor without physical disturbance, as well as physical sampling methodologies (e.g., grab or core sampling). The terms benthic mapping and benthic habitat mapping are often used synonymously to describe seafloor mapping conducted for the purpose of benthic habitat identification. There is a subtle yet important difference, however, between general benthic mapping and benthic habitat mapping. The distinction is important because it dictates the sequential analysis and visualization techniques that are employed following data collection. In this paper general seafloor mapping for identification of regional geologic features and morphology is defined as benthic mapping. Benthic habitat mapping incorporates the regional scale geologic information but also includes higher resolution surveys and analysis of biological communities to identify the biological habitats. In addition, this paper adopts the definition of habitats established by Kostylev et al. (2001) as a “spatially defined area where the physical, chemical, and biological environment is distinctly different from the surrounding environment.” (PDF contains 31 pages)
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Presentation slides as part of the Janet network end-to-end performance initiative
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In the hybrid approach of large-eddy simulation (LES) and Lighthill’s acoustic analogy for turbulence-generated sound, the turbulence source fields are obtained using an LES and the turbulence-generated sound at far fields is calculated from Lighthill’s acoustic analogy. As only the velocity fields at resolved scales are available from the LES, the Lighthill stress tensor, serving as a source term in Lighthill’s acoustic equation, has to be evaluated from the resolved velocity fields. As a result, the contribution from the unresolved velocity fields is missing in the conventional LES. The sound of missing scales is shown to be important and hence needs to be modeled. The present study proposes a kinematic subgrid-scale (SGS) model which recasts the unresolved velocity fields into Lighthill’s stress tensors. A kinematic simulation is used to construct the unresolved velocity fields with the imposed temporal statistics, which is consistent with the random sweeping hypothesis. The kinematic SGS model is used to calculate sound power spectra from isotropic turbulence and yields an improved result: the missing portion of the sound power spectra is approximately recovered in the LES.
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Large-eddy simulation (LES) has emerged as a promising tool for simulating turbulent flows in general and, in recent years,has also been applied to the particle-laden turbulence with some success (Kassinos et al., 2007). The motion of inertial particles is much more complicated than fluid elements, and therefore, LES of turbulent flow laden with inertial particles encounters new challenges. In the conventional LES, only large-scale eddies are explicitly resolved and the effects of unresolved, small or subgrid scale (SGS) eddies on the large-scale eddies are modeled. The SGS turbulent flow field is not available. The effects of SGS turbulent velocity field on particle motion have been studied by Wang and Squires (1996), Armenio et al. (1999), Yamamoto et al. (2001), Shotorban and Mashayek (2006a,b), Fede and Simonin (2006), Berrouk et al. (2007), Bini and Jones (2008), and Pozorski and Apte (2009), amongst others. One contemporary method to include the effects of SGS eddies on inertial particle motions is to introduce a stochastic differential equation (SDE), that is, a Langevin stochastic equation to model the SGS fluid velocity seen by inertial particles (Fede et al., 2006; Shotorban and Mashayek, 2006a; Shotorban and Mashayek, 2006b; Berrouk et al., 2007; Bini and Jones, 2008; Pozorski and Apte, 2009).However, the accuracy of such a Langevin equation model depends primarily on the prescription of the SGS fluid velocity autocorrelation time seen by an inertial particle or the inertial particle–SGS eddy interaction timescale (denoted by $\delt T_{Lp}$ and a second model constant in the diffusion term which controls the intensity of the random force received by an inertial particle (denoted by C_0, see Eq. (7)). From the theoretical point of view, dTLp differs significantly from the Lagrangian fluid velocity correlation time (Reeks, 1977; Wang and Stock, 1993), and this carries the essential nonlinearity in the statistical modeling of particle motion. dTLp and C0 may depend on the filter width and particle Stokes number even for a given turbulent flow. In previous studies, dTLp is modeled either by the fluid SGS Lagrangian timescale (Fede et al., 2006; Shotorban and Mashayek, 2006b; Pozorski and Apte, 2009; Bini and Jones, 2008) or by a simple extension of the timescale obtained from the full flow field (Berrouk et al., 2007). In this work, we shall study the subtle and on-monotonic dependence of $\delt T_{Lp}$ on the filter width and particle Stokes number using a flow field obtained from Direct Numerical Simulation (DNS). We then propose an empirical closure model for $\delta T_{Lp}$. Finally, the model is validated against LES of particle-laden turbulence in predicting single-particle statistics such as particle kinetic energy. As a first step, we consider the particle motion under the one-way coupling assumption in isotropic turbulent flow and neglect the gravitational settling effect. The one-way coupling assumption is only valid for low particle mass loading.
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Projects of the scope of the restoration of the Florida Everglades require substantial information regarding ecological mechanisms, and these are often poorly understood. We provide critical base knowledge for Everglades restoration by characterizing the existing vegetation communities of an Everglades remnant, describing how present and historic hydrology affect wetland vegetation community composition, and documenting change from communities described in previous studies. Vegetation biomass samples were collected along transects across Water Conservation Area 3A South (3AS).
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An extreme dry-down and muck-removal project was conducted at Lake Tohopekaliga, Florida, in 2003-2004, to remove dense vegetation from inshore areas and improve habitat degraded by stabilized water levels. Vegetation was monitored from June 2002 to December 2003, to describe the pre-existing communities in terms of composition and distribution along the environmental gradients. Three study areas (Treatment-Selection Sites) were designed to test the efficacy of different treatments in enhancing inshore habitat, and five other study areas (Whole-Lake Monitoring Sites) were designed to monitor the responses of the emergent littoral vegetation as a whole. Five general community types were identified within the study areas by recording aboveground biomasses and stem densities of each species. These communities were distributed along water and soils gradients, with water depth and bulk density explaining most of the variation. The shallowest depths were dominated by a combination of Eleocharis spp., Luziola fluitans, and Panicum repens; while the deeper areas had communities of Nymphaea odorata and Nuphar luteum; Typha spp.; or Paspalidium geminatum and Hydrilla verticillata. Mineralized soils were common in both the shallow and deep-water communities, while the intermediate depths had high percentages of organic material in the soil. These intermediate depths (occurring just above and just below low pool stage) were dominated by Pontederia cordata, the main species targeted by the habitat enhancement project. This emergent community occurred in nearly monocultural bands around the lake (from roughly 60–120 cm in depth at high pool stage) often having more diverse floating mats along the deep-water edge. The organic barrier these mats create is believed to impede access of sport fish to shallow-water spawning areas, while the overall low diversity of the community is evidence of its competitive nature in stabilized waters. With continued monitoring of these study areas long-term effects of the restoration project can be assessed and predictive models may be created to determine the efficacy and legitimacy of such projects in the future.
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For efficient use of conservation resources it is important to determine how species diversity changes across spatial scales. In many poorly known species groups little is known about at which spatial scales the conservation efforts should be focused. Here we examined how the community turnover of wood-inhabiting fungi is realised at three hierarchical levels, and how much of community variation is explained by variation in resource composition and spatial proximity. The hierarchical study design consisted of management type (fixed factor), forest site (random factor, nested within management type) and study plots (randomly placed plots within each study site). To examine how species richness varied across the three hierarchical scales, randomized species accumulation curves and additive partitioning of species richness were applied. To analyse variation in wood-inhabiting species and dead wood composition at each scale, linear and Permanova modelling approaches were used. Wood-inhabiting fungal communities were dominated by rare and infrequent species. The similarity of fungal communities was higher within sites and within management categories than among sites or between the two management categories, and it decreased with increasing distance among the sampling plots and with decreasing similarity of dead wood resources. However, only a small part of community variation could be explained by these factors. The species present in managed forests were in a large extent a subset of those species present in natural forests. Our results suggest that in particular the protection of rare species requires a large total area. As managed forests have only little additional value complementing the diversity of natural forests, the conservation of natural forests is the key to ecologically effective conservation. As the dissimilarity of fungal communities increases with distance, the conserved natural forest sites should be broadly distributed in space, yet the individual conserved areas should be large enough to ensure local persistence.