995 resultados para Spatial constraints


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Anthropogenic increases in the partial pressure of CO2 (pCO2) cause ocean acidification, declining calcium carbonate saturation states, reduced coral reef calcification and changes in the compositions of marine communities. Most projected community changes due to ocean acidification describe transitions from hard coral to non-calcifying macroalgal communities; other organisms have received less attention, despite the biotic diversity of coral reef communities. We show that the spatial distributions of both hard and soft coral communities in volcanically acidified, semi-enclosed waters off Iwotorishima Island, Japan, are related to pCO2 levels. Hard corals are restricted to non-acidified low- pCO2 (225 µatm) zones, dense populations of the soft coral Sarcophyton elegans dominate medium- pCO2 (831 µatm) zones, and both hard and soft corals are absent from the highest- pCO2 (1,465 µatm) zone. In CO2-enriched culture experiments, high- pCO2 conditions benefited Sarcophyton elegans by enhancing photosynthesis rates and did not affect light calcification, but dark decalcification (negative net calcification) increased with increasing pCO2. These results suggest that reef communities may shift from reef-building hard corals to non-reef-building soft corals under pCO2 levels (550-970 µatm) predicted by the end of this century, and that higher pCO2 levels would challenge the survival of some reef organisms.

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Source routes and Spatial Diffusion of capuchin monkeys over the past 6 million years, rebuilt in the SPREAD 1.0.6 from the MCC tree. The map shows the 10 different regions to which distinctive samples were associated. The different transmission routes have been calculated from the average rate over time. Only rates with Bayes factor> 3 were considered as significantly different from zero. Significant diffusion pathways are highlighted with color varying from dark brown to red, being the dark brown less significant rates and deep red the most significant rates.

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Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

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The study was carried out on the main plots (Main Experiment) of a large grassland biodiversity experiment, the Jena Experiment. In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. This data set consists of standard deviation (SD), mean and stability (stab) of soil microbial basal respiration (µl O2/h/g dry soil) and microbial biomass carbon (µg C/g dry soil). Data were derived by taking soil samples and measuring basal and substrate-induced microbial respiration with an oxygen-consumption apparatus. Samples for calculating the spatial stability of soil microbial properties were taken on the 20th of September in 2010. Oxygen consumption of soil microorganisms in fresh soil equivalent to 3.5 g dry weight was measured at 22°C over a period of 24 h. Basal respiration (µlO2/g dry soil/h) was calculated as mean of the oxygen consumption rates of hours 14 to 24 after the start of measurements. Substrate- induced respiration was determined by adding D-glucose to saturate catabolic enzymes of microorganisms according to preliminary studies (4 mg g-1 dry soil solved in 400 µl deionized water). Maximum initial respiratory response (µl O2/g dry soil/ h) was calculated as mean of the lowest three oxygen consumption values within the first 10 h after glucose addition. Microbial biomass carbon (µg C/g dry soil) was calculated as 38 × Maximum initial respiratory response according to prelimiray studies.

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An image processing observational technique for the stereoscopic reconstruction of the wave form of oceanic sea states is developed. The technique incorporates the enforcement of any given statistical wave law modeling the quasi Gaussianity of oceanic waves observed in nature. The problem is posed in a variational optimization framework, where the desired wave form is obtained as the minimizer of a cost functional that combines image observations, smoothness priors and a weak statistical constraint. The minimizer is obtained combining gradient descent and multigrid methods on the necessary optimality equations of the cost functional. Robust photometric error criteria and a spatial intensity compensation model are also developed to improve the performance of the presented image matching strategy. The weak statistical constraint is thoroughly evaluated in combination with other elements presented to reconstruct and enforce constraints on experimental stereo data, demonstrating the improvement in the estimation of the observed ocean surface.

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In optimal foraging theory, search time is a key variable defining the value of a prey type. But the sensory-perceptual processes that constrain the search for food have rarely been considered. Here we evaluate the flight behavior of bumblebees (Bombus terrestris) searching for artificial flowers of various sizes and colors. When flowers were large, search times correlated well with the color contrast of the targets with their green foliage-type background, as predicted by a model of color opponent coding using inputs from the bees' UV, blue, and green receptors. Targets that made poor color contrast with their backdrop, such as white, UV-reflecting ones, or red flowers, took longest to detect, even though brightness contrast with the background was pronounced. When searching for small targets, bees changed their strategy in several ways. They flew significantly slower and closer to the ground, so increasing the minimum detectable area subtended by an object on the ground. In addition, they used a different neuronal channel for flower detection. Instead of color contrast, they used only the green receptor signal for detection. We relate these findings to temporal and spatial limitations of different neuronal channels involved in stimulus detection and recognition. Thus, foraging speed may not be limited only by factors such as prey density, flight energetics, and scramble competition. Our results show that understanding the behavioral ecology of foraging can substantially gain from knowledge about mechanisms of visual information processing.

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The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.