986 resultados para spatial processes
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RésuméLa coexistence de nombreuses espèces différentes a de tout temps intrigué les biologistes. La diversité et la composition des communautés sont influencées par les perturbations et l'hétérogénéité des conditions environnementales. Bien que dans la nature la distribution spatiale des conditions environnementales soit généralement autocorrélée, cet aspect est rarement pris en compte dans les modèles étudiant la coexistence des espèces. Dans ce travail, nous avons donc abordé, à l'aide de simulations numériques, la coexistence des espèces ainsi que leurs caractéristiques au sein d'un environnement autocorrélé.Afin de prendre en compte cet élément spatial, nous avons développé un modèle de métacommunauté (un ensemble de communautés reliées par la dispersion des espèces) spatialement explicite. Dans ce modèle, les espèces sont en compétition les unes avec les autres pour s'établir dans un nombre de places limité, dans un environnement hétérogène. Les espèces sont caractérisées par six traits: optimum de niche, largeur de niche, capacité de dispersion, compétitivité, investissement dans la reproduction et taux de survie. Nous nous sommes particulièrement intéressés à l'influence de l'autocorrélation spatiale et des perturbations sur la diversité des espèces et sur les traits favorisés dans la métacommunauté. Nous avons montré que l'autocorrélation spatiale peut avoir des effets antagonistes sur la diversité, en fonction du taux de perturbations considéré. L'influence de l'autocorrélation spatiale sur la capacité de dispersion moyenne dans la métacommunauté dépend également des taux de perturbations et survie. Nos résultats ont aussi révélé que de nombreuses espèces avec différents degrés de spécialisation (i.e. différentes largeurs de niche) peuvent coexister. Toutefois, les espèces spécialistes sont favorisées en absence de perturbations et quand la dispersion est illimitée. A l'opposé, un taux élevé de perturbations sélectionne des espèces plus généralistes, associées avec une faible compétitivité.L'autocorrélation spatiale de l'environnement, en interaction avec l'intensité des perturbations, influence donc de manière considérable la coexistence ainsi que les caractéristiques des espèces. Ces caractéristiques sont à leur tour souvent impliquées dans d'importants processus, comme le fonctionnement des écosystèmes, la capacité des espèces à réagir aux invasions, à la fragmentation de l'habitat ou aux changements climatiques. Ce travail a permis une meilleure compréhension des mécanismes responsables de la coexistence et des caractéristiques des espèces, ce qui est crucial afin de prédire le devenir des communautés naturelles dans un environnement changeant.AbstractUnderstanding how so many different species can coexist in nature is a fundamental and long-standing question in ecology. Community diversity and composition are known to be influenced by heterogeneity in environmental conditions and disturbance. Though in nature the spatial distribution of environmental conditions is frequently autocorrelated, this aspect is seldom considered in models investigating species coexistence. In this work, we thus addressed several questions pertaining to species coexistence and composition in spatially autocorrelated environments, with a numerical simulations approach.To take into account this spatial aspect, we developed a spatially explicit model of metacommunity (a set of communities linked by dispersal of species). In this model, species are trophically equivalent, and compete for space in a heterogeneous environment. Species are characterized by six life-history traits: niche optimum, niche breadth, dispersal, competitiveness, reproductive investment and survival rate. We were particularly interested in the influence of environmental spatial autocorrelation and disturbance on species diversity and on the traits of the species favoured in the metacommunity. We showed that spatial autocorrelation can have antagonistic effects on diversity depending on disturbance rate. Similarly, spatial autocorrelation interacted with disturbance rate and survival rate to shape the mean dispersal ability observed in the metacommunity. Our results also revealed that many species with various degrees of specialization (i.e. different niche breadths) can coexist together. However specialist species were favoured in the absence of disturbance, and when dispersal was unlimited. In contrast, high disturbance rate selected for more generalist species, associated with low competitive ability.The spatial structure of the environment, together with disturbance and species traits, thus strongly impacts species diversity and, more importantly, species composition. Species composition is known to affect several important metacommunity properties such as ecosystem functioning, resistance and reaction to invasion, to habitat fragmentation and to climate changes. This work allowed a better understanding of the mechanisms responsible for species composition, which is of crucial importance to predict the fate of natural metacommunities in changing environments
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Aim Our aims were to compare the composition of testate amoeba (TA) communities from Santa Cruz Island, Galápagos Archipelago, which are likely in existence only as a result of anthropogenic habitat transformation, with similar naturally occurring communities from northern and southern continental peatlands. Additionally, we aimed at assessing the importance of niche-based and dispersal-based processes in determining community composition and taxonomic and functional diversity. Location The humid highlands of the central island of Santa Cruz, Galápagos Archipelago. Methods We survey the alpha, beta and gamma taxonomic and functional diversities of TA, and the changes in functional traits along a gradient of wet to dry habitats. We compare the TA community composition, abundance and frequency recorded in the insular peatlands with that recorded in continental peatlands of Northern and Southern Hemispheres. We use generalized linear models to determine how environmental conditions influence taxonomic and functional diversity as well as the mean values of functional traits within communities. We finally apply variance partitioning to assess the relative importance of niche- and dispersal-based processes in determining community composition. Results TA communities in Santa Cruz Island were different from their Northern Hemisphere and South American counterparts with most genera considered as characteristic for Northern Hemisphere and South American Sphagnum peatlands missing or very rare in the Galápagos. Functional traits were most correlated with elevation and site topography and alpha functional diversity to the type of material sampled and site topography. Community composition was more strongly correlated with spatial variables than with environmental ones. Main conclusions TA communities of the Sphagnum peatlands of Santa Cruz Island and the mechanisms shaping these communities contrast with Northern Hemisphere and South American peatlands. Soil moisture was not a strong predictor of community composition most likely because rainfall and clouds provide sufficient moisture. Dispersal limitation was more important than environmental filtering because of the isolation of the insular peatlands from continental ones and the young ecological history of these ecosystems.
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In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Spatial processes could play an important role in density-dependent population regulation because the disproportionate use of poor quality habitats as population size increases is widespread in animal populations-the so-called buffer effect. While the buffer effect patterns and their demographic consequences have been described in a number of wild populations, much less is known about how dispersal affects distribution patterns and ultimately density dependence. Here, we investigated the role of dispersal in spatial density dependence using an extraordinarily detailed dataset from a reintroduced Mauritius kestrel (Falco punctatus) population with a territorial (despotic) breeding system. We show that recruitment rates varied significantly between territories, and that territory occupancy was related to its recruitment rate, both of which are consistent with the buffer effect theory. However, we also show that restricted dispersal affects the patterns of territory occupancy with the territories close to release sites being occupied sooner and for longer as the population has grown than the territories further away. As a result of these dispersal patterns, the strength of spatial density dependence is significantly reduced. We conclude that restricted dispersal can modify spatial density dependence in the wild, which has implications for the way population dynamics are likely to be impacted by environmental change.
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Abstract: Movements away from the natal or home territory are important to many ecological processes, including gene flow, population regulation, and disease epidemiology, yet quantitative data on these behaviors are lacking. Red foxes exhibit 2 periods of extraterritorial movements: when an individual disperses and when males search neighboring territories for extrapair copulations during the breeding season. Using radiotracking data collected at 5-min interfix intervals, we compared movement parameters, including distance moved, speed of movement, and turning angles, of dispersal and reproductive movements to those made during normal territorial movements; the instantaneous separation distances of dispersing and extraterritorial movements to the movements of resident adults; and the frequency of locations of 95%, 60%, and 30% harmonic mean isopleths of adult fox home territories to randomly generated fox movements. Foxes making reproductive movements traveled farther than when undertaking other types of movement, and dispersal movements were straighter. Reproductive and dispersal movements were faster than territorial movements and also differed in intensity of search and thoroughness. Foxes making dispersal movements avoided direct contact with territorial adults and moved through peripheral areas of territories. The converse was true for reproductive movements. Although similar in some basic characteristics, dispersal and reproductive movements are fundamentally different both behaviorally and spatially and are likely to have different ultimate purposes and contrasting effects on spatial processes such as disease transmission
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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All trees with diameter at breast height dbh >= 10.0 cm were stem-mapped in a "terra firme" tropical rainforest in the Brazilian Amazon, at the EMBRAPA Experimental Site, Manaus, Brazil. Specifically, the relationships of tree species with soil properties were determined by using canonical correspondence analyses based on nine soil variables and 68 tree species. From the canonical correspondence analyses, the species were grouped into two groups: one where species occur mainly in sandy sites, presenting low organic matter content; and another one where species occur mainly in dry and clayey sites. Hence, we used Ripley's K function to analyze the distribution of species in 32 plots ranging from 2,500 m(2) to 20,000 m(2) to determine whether each group presents some spatial aggregation as a soil variations result. Significant spatial aggregation for the two groups was found only at over 10,000 m(2) sampling units, particularly for those species found in clayey soils and drier environments, where the sampling units investigated seemed to meet the species requirements. Soil variables, mediated by topographic positions had influenced species spatial aggregation, mainly in an intermediate to large distances varied range (>= 20 m). Based on our findings, we conclude that environmental heterogeneity and 10,000 m(2) minimum sample unit sizes should be considered in forest dynamic studies in order to understand the spatial processes structuring the "terra firme" tropical rainforest in Brazilian Amazon.
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Amongst the various hypotheses that challenged to explain the coexistence of species with similar life histories, theoretical, and empirical studies suggest that spatial processes may slow down competitive exclusion and hence promote coexistence even in the absence of evident trade-offs and frequent disturbances. We investigated the effects of spatial pattern and density on the relative importance of intra- and interspecific competition in a field experiment. We hypothesized that weak competitors increased biomass and seed production within neighborhoods of conspecifics, while stronger competitors would show increased biomass and seed production within neighborhoods of heterospecifics. Seeds of four annual plant species (Capsella bursa-pastoris, Stachys annua, Stellaria media, Poa annua) were sown in two spatial patterns (aggregated vs. random) and at two densities (low vs. high) in three different species combinations (monocultures, three and four species mixtures). There was a hierarchy in biomass production among the four species and C. bursa-pastoris and S. media were among the weak competitors. Capsella and Stellaria showed increased biomass production and had more individuals in the aggregated compared to the random pattern, especially when both superior competitors (S. annua, P. annua) were present. For P. annua we observed considerable differences among species combinations and unexpected pattern effects. Our findings support the hypothesis that weak competitors increase their fitness when grown in the neighborhood of conspecifics, and suggested that for the weakest competitors the species identity is not important and all other species are best avoided through intraspecific aggregation. In addition, our data suggest that the importance of spatial pattern for the other competitors might not only depend on the position within the hierarchy but also on the identity of neighbor species, species characteristics, below ground interactions, and other nonspatial factors.
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The paper presents a computational system based upon formal principles to run spatial models for environmental processes. The simulator is named SimuMap because it is typically used to simulate spatial processes over a mapped representation of terrain. A model is formally represented in SimuMap as a set of coupled sub-models. The paper considers the situation where spatial processes operate at different time levels, but are still integrated. An example of such a situation commonly occurs in watershed hydrology where overland flow and stream channel flow have very different flow rates but are highly related as they are subject to the same terrain runoff processes. SimuMap is able to run a network of sub-models that express different time-space derivatives for water flow processes. Sub-models may be coded generically with a map algebra programming language that uses a surface data model. To address the problem of differing time levels in simulation, the paper: (i) reviews general approaches for numerical solvers, (ii) considers the constraints that need to be enforced to use more adaptive time steps in discrete time specified simulations, and (iii) scaling transfer rates in equations that use different time bases for time-space derivatives. A multistep scheme is proposed for SimuMap. This is presented along with a description of its visual programming interface, its modelling formalisms and future plans. (C) 2003 Elsevier Ltd. All rights reserved.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a country-level, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO2, SO2, NO (x), and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension). We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries. The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO2 emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO2, in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO2 and SO2 is due to local electricity processes, justifying our emphasis on local energy infrastructure. The complexity of considering differences in location, product, and pollutant is rewarded by a much richer understanding of a global production-consumption chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especially in developing countries. Our spatial LCI method yields important insights in the quantity and pattern of emissions due to different product life cycle stages, dependent on the local technology, emphasizing the importance of consumer behavior. From a life cycle perspective, consumer education promoting air-drying and cool washing is more important than efficient appliances. Spatial LCI with country-specific data is a promising method, necessary for the challenges of globalized production-consumption chains. We recommend inventory reporting of final energy forms, such as electricity, and modular LCA databases, which would allow the easy modification of underlying energy infrastructure.
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Si les principes d’utilisabilité guident la conception de solutions de design interactif pour s’assurer que celles-ci soient « utilisables », quels principes guident la conception d’objets interactifs pour s’assurer que l’expérience subjective de l’usager (UX) soit adéquate et mémorable? Que manque-t-il au cadre de l‘UX pour expliquer, comprendre, et anticiper en tant que designer une expérience mémorable (‘an experience’; Dewey, 1934)? La question centrale est issue d’une double problématique : (1) le cadre théorique de l’UX est incomplet, et (2) les processus et capacités des designers ne sont pas considérés et utilisés à leur pleine capacité en conception UX. Pour répondre à cette question, nous proposons de compléter les modèles de l’UX avec la notion d’expérience autotélique qui appartient principalement à deux cadres théoriques ayant bien cerné l’expérience subjective, soit l’expérience optimale (ou Flow) de Csikszentmihalyi (1988) et l’expérience esthétique selon Schaeffer (2001). L’autotélie est une dimension interne du Flow alors qu’elle couvre toute l’expérience esthétique. L’autotélie est une expérience d’éveil au moment même de l’interaction. Cette prise de conscience est accompagnée d’une imperceptible tension de vouloir faire durer ce moment pour faire durer le plaisir qu’il génère. Trois études exploratoires ont été faites, s’appuyant sur une analyse faite à partir d’un cadre théorique en trois parties : le Flow, les signes d’activité non verbale (les gestes physiques) et verbale (le discours) ont été évalués pour voir comment ceux-ci s’associent. Nos résultats tendent à prouver que les processus spatiaux jouent un rôle de premier plan dans l’expérience autotélique et par conséquent dans une UX optimale. De plus, ils suggèrent que les expériences pragmatique et autotélique sont ancrées dans un seul et même contenu, et que leur différence tient au type d’attention que le participant porte sur l’interaction, l’attention ordinaire ou de type autotélique. Ces résultats nous ont menés à proposer un modèle pour la conception UX. L’élément nouveau, resté jusqu’alors inaperçu, consiste à s’assurer que l’interface (au sens large) appelle une attitude réceptive à l’inattendu, pour qu’une information puisse déclencher les processus spatiaux, offrant une opportunité de passer de l’attention ordinaire à l’attention autotélique. Le nouveau modèle ouvre la porte à une meilleure valorisation des habiletés et processus du designer au sein de l’équipe multidisciplinaire en conception UX.