998 resultados para Niche modelling


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To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

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A significant challenge in the prediction of climate change impacts on ecosystems and biodiversity is quantifying the sources of uncertainty that emerge within and between different models. Statistical species niche models have grown in popularity, yet no single best technique has been identified reflecting differing performance in different situations. Our aim was to quantify uncertainties associated with the application of 2 complimentary modelling techniques. Generalised linear mixed models (GLMM) and generalised additive mixed models (GAMM) were used to model the realised niche of ombrotrophic Sphagnum species in British peatlands. These models were then used to predict changes in Sphagnum cover between 2020 and 2050 based on projections of climate change and atmospheric deposition of nitrogen and sulphur. Over 90% of the variation in the GLMM predictions was due to niche model parameter uncertainty, dropping to 14% for the GAMM. After having covaried out other factors, average variation in predicted values of Sphagnum cover across UK peatlands was the next largest source of variation (8% for the GLMM and 86% for the GAMM). The better performance of the GAMM needs to be weighed against its tendency to overfit the training data. While our niche models are only a first approximation, we used them to undertake a preliminary evaluation of the relative importance of climate change and nitrogen and sulphur deposition and the geographic locations of the largest expected changes in Sphagnum cover. Predicted changes in cover were all small (generally <1% in an average 4 m2 unit area) but also highly uncertain. Peatlands expected to be most affected by climate change in combination with atmospheric pollution were Dartmoor, Brecon Beacons and the western Lake District.

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High spatial resolution environmental data gives us a better understanding of the environmental factors affecting plant distributions at fine spatial scales. However, large environmental datasets dramatically increase compute times and output species model size stimulating the need for an alternative computing solution. Cluster computing offers such a solution, by allowing both multiple plant species Environmental Niche Models (ENMs) and individual tiles of high spatial resolution models to be computed concurrently on the same compute cluster. We apply our methodology to a case study of 4,209 species of Mediterranean flora (around 17% of species believed present in the biome). We demonstrate a 16 times speed-up of ENM computation time when 16 CPUs were used on the compute cluster. Our custom Java ‘Merge’ and ‘Downsize’ programs reduce ENM output files sizes by 94%. The median 0.98 test AUC score of species ENMs is aided by various species occurrence data filtering techniques. Finally, by calculating the percentage change of individual grid cell values, we map the projected percentages of plant species vulnerable to climate change in the Mediterranean region between 1950–2000 and 2020.

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Peponapis bees are considered specialized pollinators of Cucurbita flowers, a genus that presents several species of economic value (squashes and pumpkins). Both genera originated in the Americas, and their diversity dispersion center is in Mexico. Ten species of Peponapis and ten species of Cucurbita (only non-domesticated species) were analyzed considering the similarity of their ecological niche characteristics with respect to climatic conditions of their occurrence areas (abiotic variables) and interactions between species (biotic variables). The similarity of climatic conditions (temperature and precipitation) was estimated through cluster analyses. The areas of potential occurrence of the most similar species were obtained through ecological niche modeling and summed with geographic information system tools. Three main clusters were obtained: one with species that shared potential occurrence areas mainly in deserts (P. pruinosa, P. timberlakei, C. digitata, C. palmata, C. foetidissima), another in moist forests (P. limitaris, P. atrata, C. lundelliana, C. o. martinezii) and a third mainly in dry forests (C. a. sororia, C. radicans, C. pedatifolia, P. azteca, P. smithi, P. crassidentata, P. utahensis). Some species with similar ecological niche presented potential shared areas that are also similar to their geographical distribution, like those occurring predominantly on deserts. However, some clustered species presented larger geographical areas, such as P. pruinosa and C. foetidissima suggesting other drivers than climatic conditions to shape their distributions. The domestication of Cucurbita and also the natural history of both genera were considered also as important factors. (C) 2011 Elsevier B.V. All rights reserved.

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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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1. The niche variation hypothesis predicts that among-individual variation in niche use will increase in the presence of intraspecific competition and decrease in the presence of interspecific competition. We sought to determine whether the local isotopic niche breadth of fish inhabiting a wetland was best explained by competition for resources and the niche variation hypothesis, by dispersal of individuals from locations with different prey resources or by a combination of the two. We analysed stable isotopes of carbon and nitrogen as indices of feeding niche and compared metrics of within-site spread to characterise site-level isotopic niche breadth. We then evaluated the explanatory power of competing models of the direct and indirect effects of several environmental variables spanning gradients of disturbance, competition strength and food availability on among-individual variation of the eastern mosquitofish (Gambusia holbrooki). 2. The Dispersal model posits that only the direct effect of disturbance (i.e. changes in water level known to induce fish movement) influences among-individual variation in isotopic niche. The Partitioning model allows for only direct effects of local food availability on among-individual variation. The Combined model allows for both hypotheses by including the direct effects of disturbance and food availability. 3. A linear regression of the Combined model described more variance than models limited to the variables of either the Dispersal or Partitioning models. Of the independent variables considered, the food availability variable (per cent edible periphyton) explained the most variation in isotopic niche breadth, followed closely by the disturbance variable (days since last drying event). 4. Structural equation modelling provided further evidence that the Combined model was best supported by the data, with the Partitioning and the Dispersal models only modestly less informative. Again, the per cent edible periphyton was the variable with the largest direct effect on niche variability, with other food availability variables and the disturbance variable only slightly less important. Indirect effects of heterospecific and conspecific competitor densities were also important, through their effects on prey density. 5. Our results support the Combined hypotheses, although partitioning mechanisms appear to explain the most diet variation among individuals in the eastern mosquitofish. The results also support some predictions of the niche variation hypothesis, although both conspecific and interspecific competition appeared to increase isotopic niche breadth in contrast to predictions that interspecific competition would decrease it. We think this resulted from high diet overlap of co-occurring species, most of which consume similar macroinvertebrates.

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Funded by Biodiversity and Ecosystem Services in a Changing Climate Wenner-Gren Foundation Swedish Research Council The Royal Swedish Academy of Sciences Stiftelsen Anna-Greta Holger Crafoords Fund The Crafoord Foundation

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Funded by Biodiversity and Ecosystem Services in a Changing Climate Wenner-Gren Foundation Swedish Research Council The Royal Swedish Academy of Sciences Stiftelsen Anna-Greta Holger Crafoords Fund The Crafoord Foundation

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The authors would like to express their gratitude to organizations and people that supported this research. Piotr Omenzetter’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Ben Ryder of Aurecon and Graeme Cummings of HEB Construction assisted in obtaining access to the bridge and information for modelling. Luke Williams and Graham Bougen, undergraduate research students, assisted with testing.

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Funded by Biodiversity and Ecosystem Services in a Changing Climate Wenner-Gren Foundation Swedish Research Council The Royal Swedish Academy of Sciences Stiftelsen Anna-Greta Holger Crafoords Fund The Crafoord Foundation

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Aim The spread of non-indigenous species in marine ecosystems world-wide is one of today's most serious environmental concerns. Using mechanistic modelling, we investigated how global change relates to the invasion of European coasts by a non-native marine invertebrate, the Pacific oyster Crassostrea gigas. Location Bourgneuf Bay on the French Atlantic coast was considered as the northern boundary of C. gigas expansion at the time of its introduction to Europe in the 1970s. From this latitudinal reference, variations in the spatial distribution of the C. gigas reproductive niche were analysed along the north-western European coast from Gibraltar to Norway. Methods The effects of environmental variations on C. gigas physiology and phenology were studied using a bioenergetics model based on Dynamic Energy Budget theory. The model was forced with environmental time series including in situ phytoplankton data, and satellite data of sea surface temperature and suspended particulate matter concentration. Results Simulation outputs were successfully validated against in situ oyster growth data. In Bourgneuf Bay, the rise in seawater temperature and phytoplankton concentration has increased C. gigas reproductive effort and led to precocious spawning periods since the 1960s. At the European scale, seawater temperature increase caused a drastic northward shift (1400 km within 30 years) in the C. gigas reproductive niche and optimal thermal conditions for early life stage development. Main conclusions We demonstrated that the poleward expansion of the invasive species C. gigas is related to global warming and increase in phytoplankton abundance. The combination of mechanistic bioenergetics modelling with in situ and satellite environmental data is a valuable framework for ecosystem studies. It offers a generic approach to analyse historical geographical shifts and to predict the biogeographical changes expected to occur in a climate-changing world.

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The basic reproduction number is a key parameter in mathematical modelling of transmissible diseases. From the stability analysis of the disease free equilibrium, by applying Routh-Hurwitz criteria, a threshold is obtained, which is called the basic reproduction number. However, the application of spectral radius theory on the next generation matrix provides a different expression for the basic reproduction number, that is, the square root of the previously found formula. If the spectral radius of the next generation matrix is defined as the geometric mean of partial reproduction numbers, however the product of these partial numbers is the basic reproduction number, then both methods provide the same expression. In order to show this statement, dengue transmission modelling incorporating or not the transovarian transmission is considered as a case study. Also tuberculosis transmission and sexually transmitted infection modellings are taken as further examples.

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Shot peening is a cold-working mechanical process in which a shot stream is propelled against a component surface. Its purpose is to introduce compressive residual stresses on component surfaces for increasing the fatigue resistance. This process is widely applied in springs due to the cyclical loads requirements. This paper presents a numerical modelling of shot peening process using the finite element method. The results are compared with experimental measurements of the residual stresses, obtained by the X-rays diffraction technique, in leaf springs submitted to this process. Furthermore, the results are compared with empirical and numerical correlations developed by other authors.

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Mycoplasma suis, the causative agent of porcine infectious anemia, has never been cultured in vitro and mechanisms by which it causes disease are poorly understood. Thus, the objective herein was to use whole genome sequencing and analysis of M. suis to define pathogenicity mechanisms and biochemical pathways. M. suis was harvested from the blood of an experimentally infected pig. Following DNA extraction and construction of a paired end library, whole-genome sequencing was performed using GS-FLX (454) and Titanium chemistry. Reads on paired-end constructs were assembled using GS De Novo Assembler and gaps closed by primer walking; assembly was validated by PFGE. Glimmer and Manatee Annotation Engine were used to predict and annotate protein-coding sequences (CDS). The M. suis genome consists of a single, 742,431 bp chromosome with low G+C content of 31.1%. A total of 844 CDS, 3 single copies, unlinked rRNA genes and 32 tRNAs were identified. Gene homologies and GC skew graph show that M. suis has a typical Mollicutes oriC. The predicted metabolic pathway is concise, showing evidence of adaptation to blood environment. M. suis is a glycolytic species, obtaining energy through sugars fermentation and ATP-synthase. The pentose-phosphate pathway, metabolism of cofactors and vitamins, pyruvate dehydrogenase and NAD(+) kinase are missing. Thus, ribose, NADH, NADPH and coenzyme A are possibly essential for its growth. M. suis can generate purines from hypoxanthine, which is secreted by RBCs, and cytidine nucleotides from uracil. Toxins orthologs were not identified. We suggest that M. suis may cause disease by scavenging and competing for host nutrients, leading to decreased life-span of RBCs. In summary, genome analysis shows that M. suis is dependent on host cell metabolism and this characteristic is likely to be linked to its pathogenicity. The prediction of essential nutrients will aid the development of in vitro cultivation systems.

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Southeastern Brazil has seen dramatic landscape modifications in recent decades, due to expansion of agriculture and urban areas; these changes have influenced the distribution and abundance of vertebrates. We developed predictive models of ecological and spatial distributions of capybaras (Hydrochoerus hydrochaeris) using ecological niche modeling. Most Occurrences of capybaras were in flat areas with water bodies Surrounded by sugarcane and pasture. More than 75% of the Piracicaba River basin was estimated as potentially habitable by capybara. The models had low omission error (2.3-3.4%), but higher commission error (91.0-98.5%); these ""model failures"" seem to be more related to local habitat characteristics than to spatial ones. The potential distribution of capybaras in the basin is associated with anthropogenic habitats, particularly with intensive land use for agriculture.