944 resultados para Landscape Ecological Classification


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This paper reviews the ways that quality can be assessed in standing waters, a subject that has hitherto attracted little attention but which is now a legal requirement in Europe. It describes a scheme for the assessment and monitoring of water and ecological quality in standing waters greater than about I ha in area in England & Wales although it is generally relevant to North-west Europe. Thirteen hydrological, chemical and biological variables are used to characterise the standing water body in any current sampling. These are lake volume, maximum depth, onductivity, Secchi disc transparency, pH, total alkalinity, calcium ion concentration, total N concentration,winter total oxidised inorganic nitrogen (effectively nitrate) concentration, total P concentration, potential maximum chlorophyll a concentration, a score based on the nature of the submerged and emergent plant community, and the presence or absence of a fish community. Inter alia these variables are key indicators of the state of eutrophication, acidification, salinisation and infilling of a water body.

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Slavic and German colonization of the southern Baltic between the 8th and 15th centuries A.D. is well-documented archaeologically and historically. Despite the large number of pollen profiles from Poland, few palaeoecological studies have examined the ecological impact of a process that was central to the expansion of European, Christian, societies. This study aims to redress this balance through multiproxy analysis of lake sediments from Radzyń Chełminski, Northern Poland, using pollen, element geochemistry (Inductively Coupled-Optical Emission Spectroscopy [ICP-OES]), organic content, and magnetic susceptibility. The close association between lake and medieval settlements presents the ideal opportunity to reconstruct past vegetation and land-use dynamics within a well-documented archaeological, historical, and cultural context. Three broad phases of increasing landscape impact are visible in the pollen and geochemical data dating from the 8th/9th, 10th/11th, and 13th centuries, reflecting successive phases of Slavic and German colonization. This involved the progressive clearance of oak-hornbeam dominated woodland and the development of an increasingly open agricultural landscape. Although the castles and towns of the Teutonic Order remain the most visible signs of medieval colonization, the palynological and geochemical data demonstrate that the major phase of woodland impact occurred during the preceding phase of Slavic expansion; Germans colonists were entering a landscape already significantly altered.

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Biogeography has been difficult to apply as a methodological approach because organismic biology is incomplete at levels where the process of formulating comparisons and analogies is complex. The study of insect biogeography became necessary because insects possess numerous evolutionary traits and play an important role as pollinators. Among insects, the euglossine bees, or orchid bees, attract interest because the study of their biology allows us to explain important steps in the evolution of social behavior and many other adaptive tradeoffs. We analyzed the distribution of morphological characteristics in Colombian orchid bees from an ecological perspective. The aim of this study was to observe the distribution of these attributes on a regional basis. Data corresponding to Colombian euglossine species were ordered with a correspondence analysis and with subsequent hierarchical clustering. Later, and based on community proprieties, we compared the resulting hierarchical model with the collection localities to seek to identify a biogeographic classification pattern. From this analysis, we derived a model that classifies the territory of Colombia into 11 biogeographic units or natural clusters. Ecological assumptions in concordance with the derived classification levels suggest that species characteristics associated with flight performance, nectar uptake, and social behavior are the factors that served to produce the current geographical structure.

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Soils of a large tropical area with differentiated landscapes cannot be treated uniformly for ecological applications. We intend to develop a framework based on physiography that can be used in regional applications. The study region occupies more than 1.1 million km² and is located at the junction of the savanna region of Central Brazil and the Amazon forest. It includes a portion of the high sedimentary Central Brazil plateau and large areas of mostly peneplained crystalline shield on the border of the wide inner-Amazon low sedimentary plain. A first broad subdivision was made into landscape regions followed by a more detailed subdivision into soil regions. Mapping information was extracted from soil survey maps at scales of 1:250000-1:500000. Soil units were integrated within a homogenized legend using a set of selected attributes such as taxonomic term, the texture of the B horizon and the associated vegetation. For each region, a detailed inventory of the soil units with their area distribution was elaborated. Ten landscape regions and twenty-four soil regions were recognized and delineated. Soil cover of a region is normally characterized by a cluster composed of many soil units. Soil diversity is comparable in the landscape and the soil regions. Composition of the soil cover is quantitatively expressed in terms of area extension of the soil units. Such geographic divisions characterized by grouping soil units and their spatial estimates must be used for regional ecological applications.

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The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.

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The aim of the present study is to identify and evaluate the relationship between Woodpigeon (Columba palumbus, Linnaeus, 1758) density and different environmental gradients (thermotype, ombrotype, continentality and latitudinal), land use and landscape structure, using geographic information systems and multivariate modelling. Transects (n = 396) were developed to estimate the density of Woodpigeon in the Marina Baja (Alicante, Spain) from 2006 to 2008. The highestdensity for Woodpigeon was in September-October (1.28birds/10ha) and the lowest inFebruary-March (0.34birds/10ha). Moreover, there were more Woodpigeons in areas with a mesomediterranean thermotypethan in thermomediterranean or supramediterranean ones. There was greater densityinthe intermediate zones compared to thecoast and interior. The natural or cultural landscape had the highest Woodpigeon density (1.53birds/10ha), with both denseand clear pine forest values standing out. Therefore, it is very important to conserve these traditional landscapes with adequate management strategies in order to maintain, resident and transient Woodpigeon populations. These natural areas are open places where the Woodpigeons find food and detect the presence ofpredators. Thus, this study will enable more precise knowledge of the ecological factors (habitat variables) that intervene in the distribution of Woodpigeon populations and their density.

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Landscape analysis with transects, in the Marina Baja area (province of Alicante, Spain), has contributed to establish the influence of different landscape matrices and some environmental gradients on wild rabbit Oryctolagus cuniculus (Linnaeus, 1758) (Mammalia: Leporidae) abundance (kilometric abundance index, KAI). Transects (n = 396) were developed to estimate the abundance of this species in the study area from 2006 to 2008.Our analysis shows that rabbits have preferences for a specific land use matrix (irrigated: KAI = 3.47 ± 1.14 rabbits/km). They prefer the coastal area (KAI = 3.82 ± 1.71 rabbits/km), which coincides with thermo-Mediterranean (a bioclimatic belt with a tempered winter and a hot and dry summer with high human density), as opposed to areas in the interior (continental climate with lower human occupation). Their preference for the southern area of the region was also noted (KAI = 8.22 ± 3.90 rabbits/km), which coincides with the upper semi-arid area, as opposed to the northern and intermediate areas (the north of the region coinciding with the upper dry and the intermediate area with the lower dry). On the other hand, we found that the number of rabbits increased during the 3-year study period, with the highest abundance (KAI = 2.71 ± 1.30 rabbits/km) inMay. Thus, this study will enable more precise knowledge of the ecological factors (habitat variables) that intervene in the distribution of wild rabbit populations in a poorly studied area.

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Mode of access: Internet.

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Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

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Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation process to obtain geographical descriptions for bioregions, each of these is transformed into a Normal density estimate on environmental variables within that region. This prior information is balanced with data classification of environmental datasets using a Bayesian statistical modelling approach to objectively map ecological regions. The method is called model-based clustering as it fits a Normal mixture model to the clusters associated with regions, and it addresses issues of uncertainty in environmental datasets due to overlapping clusters.

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Planning for complex ecosystem restoration projects involves integrating ecological modeling with analysis of performance trade-offs among restoration alternatives. The authors used the Everglades Landscape Model and Multi-Criteria Decision Analysis to explore the effect of simulated ecosystem performance, risk preferences, and criteria weights on the ranking of three alternatives to restoring overland sheet flow in the Everglades. The ecological model outputs included both hydrologic and water quality criteria. Results were scored in the decision analysis framework, highlighting the trade-offs between hydrologic restoration and water quality constraints. Given equal weighting of performance measures, the alternative with more homogenous sheet flow was preferred over other alternatives, despite evidence of some localized eutrophication risk.

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Dependence of some species on landscape structure has been proved in numerous studies. So far, however, little progress has been made in the integration of landscape metrics in the prediction of species associated with coastal features. Specific landscape metrics were tested as predictors of coastal shape using three coastal features of the Iberian Peninsula (beaches, capes and gulfs) at different scales. We used the landscape metrics in combination with environmental variables to model the niche and find suitable habitats for a seagrass species (Cymodocea nodosa) throughout its entire range of distribution. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We provided the first global model of the factors that can be shaping the environmental niche and distribution of C. nodosa throughout its range. Sea surface temperature and salinity were the most relevant variables. We identified areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. We also present some preliminary results of testing historical biogeographical hypotheses derived from distribution predictions under Last Glacial Maximum conditions and genetic diversity data.