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Validation Techniques (validation + techniques)
Selected AbstractsBIOMOD , optimizing predictions of species distributions and projecting potential future shifts under global changeGLOBAL CHANGE BIOLOGY, Issue 10 2003Wilfried ThuillerArticle first published online: 9 OCT 200 Abstract A new computation framework (BIOMOD: BIOdiversity MODelling) is presented, which aims to maximize the predictive accuracy of current species distributions and the reliability of future potential distributions using different types of statistical modelling methods. BIOMOD capitalizes on the different techniques used in static modelling to provide spatial predictions. It computes, for each species and in the same package, the four most widely used modelling techniques in species predictions, namely Generalized Linear Models (GLM), Generalized Additive Models (GAM), Classification and Regression Tree analysis (CART) and Artificial Neural Networks (ANN). BIOMOD was applied to 61 species of trees in Europe using climatic quantities as explanatory variables of current distributions. On average, all the different modelling methods yielded very good agreement between observed and predicted distributions. However, the relative performance of different techniques was idiosyncratic across species, suggesting that the most accurate model varies between species. The results of this evaluation also highlight that slight differences between current predictions from different modelling techniques are exacerbated in future projections. Therefore, it is difficult to assess the reliability of alternative projections without validation techniques or expert opinion. It is concluded that rather than using a single modelling technique to predict the distribution of several species, it would be more reliable to use a framework assessing different models for each species and selecting the most accurate one using both evaluation methods and expert knowledge. [source] A bioenergetics model for juvenile flounder Platichthys flesusJOURNAL OF APPLIED ICHTHYOLOGY, Issue 1 2006M. Stevens Summary Despite the numerous physiological studies on flatfish and their economic and ecologic importance, only a few attempts have been made to construct a bioenergetics model for these species. Here we present the first bioenergetics model for European flounder (Platichthys flesus), using experimentally derived parameter values. We tested model performance using literature derived field-based estimates of food consumption and growth rates of an estuarine flounder population in the Ythan estuary, Scotland. The model was applied to four age-classes of flounder (age 0,3). Sensitivity of model predictions to parameter perturbation was estimated using error analysis. The fit between observed and predicted series was evaluated using three statistical methods: partitioning mean squared error, a reliability index (RI) and an index of modelling efficiency (MEF). Overall, model predictions closely tracked the observed changes of consumption and growth. The results of the different validation techniques show a high goodness-of-fit between observed and simulated values. The model clearly demonstrates the importance of temperature in determining growth of flounder in the estuary. A sex-specific estimation of the energetic costs of spawning in adult flounder and a more accurate description of the thermal history of the fish may further reduce the error in the model predictions. [source] Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysisMOLECULAR ECOLOGY, Issue 17 2010STEPHEN F. SPEAR Abstract Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies. [source] Ovarian cancer proteomics: Many technologies one goalPROTEOMICS - CLINICAL APPLICATIONS, Issue 2 2008Kothandaraman Narasimhan Abstract The last decade has seen major changes in the technologies used to identify markers for diagnosing cancer. This review focuses on recent developments on the evolving field of biomarker discovery, and validation techniques using proteomics platforms for ovarian cancer. It is possible now to diagnose various disease conditions using microliter quantities of body fluids. Currently the major developments were made in three distinct areas: (i) protein profiling, (ii) high-throughput validation techniques, and (iii) solid and liquid phase protein microarray platforms for analyzing candidate markers across subclasses and stages of cancers. The recent addition to the long list of technologies is metabolomics using metabolite profiling and informatics-based filtering of information for biomarker discovery of ovarian cancer. Emerging technologies need to address ways to eliminate the limitations posed by the complex dynamic nature of body fluids as well as ways to enrich low-abundance tumor markers if they were to become a successful biomarker discovery tool. These new technologies hold significant promise in identifying more robust markers for ovarian cancer. Since the prevalence of this disease in the population is low, the test must have a high specificity. [source] On vital aid: the why, what and how of validationACTA CRYSTALLOGRAPHICA SECTION D, Issue 2 2009Gerard J. Kleywegt Limitations to the data and subjectivity in the structure-determination process may cause errors in macromolecular crystal structures. Appropriate validation techniques may be used to reveal problems in structures, ideally before they are analysed, published or deposited. Additionally, such techniques may be used a posteriori to assess the (relative) merits of a model by potential users. Weak validation methods and statistics assess how well a model reproduces the information that was used in its construction (i.e. experimental data and prior knowledge). Strong methods and statistics, on the other hand, test how well a model predicts data or information that were not used in the structure-determination process. These may be data that were excluded from the process on purpose, general knowledge about macromolecular structure, information about the biological role and biochemical activity of the molecule under study or its mutants or complexes and predictions that are based on the model and that can be tested experimentally. [source] | ||||||||||