			Home About us Contact

Model Robustness (model + robustness)

Distribution by Scientific Domains

Life Sciences	40%

Selected Abstracts

One Hundred Fifty Years of Change in Forest Bird Breeding Habitat: Estimates of Species Distributions

CONSERVATION BIOLOGY, Issue 6 2005
LISA A. SCHULTE
aptitud del hábitat; ecología aviar; ecología de paisaje; planificación de conservación Abstract:,Evaluating bird population trends requires baseline data. In North America the earliest population data available are those from the late 1960s. Forest conditions in the northern Great Lake states (U.S.A.), however, have undergone succession since the region was originally cut over around the turn of the twentieth century, and it is expected that bird populations have undergone concomitant change. We propose pre-Euro-American settlement as an alternative baseline for assessing changes in bird populations. We evaluated the amount, quality, and distribution of breeding bird habitat during the mid-1800s and early 1990s for three forest birds: the Pine Warbler (Dendroica pinus), Blackburnian Warbler (D. fusca), and Black-throated Green Warbler (D. virens). We constructed models of bird and habitat relationships based on literature review and regional data sets of bird abundance and applied these models to widely available vegetation data. Original public-land survey records represented historical habitat conditions, and a combination of forest inventory and national land-cover data represented current conditions. We assessed model robustness by comparing current habitat distribution to actual breeding bird locations from the Wisconsin Breeding Bird Atlas. The model showed little change in the overall amount of Pine Warbler habitat, whereas both the Blackburnian Warber and the Black-throated Green Warbler have experienced substantial habitat losses. For the species we examined, habitat quality has degraded since presettlement and the spatial distribution of habitat shifted among ecoregions, with range expansion accompanying forest incursion into previously open habitats or the replacement of native forests with pine plantations. Sources of habitat loss and degradation include loss of conifers and loss of large trees. Using widely available data sources in a habitat suitability model framework, our method provides a long-term analysis of change in bird habitat and a presettlement baseline for assessing current conservation priority. Resumen:,La evaluación de tendencias de las poblaciones de aves requiere de datos de referencia. En Norte América, los primeros datos disponibles de poblaciones son del final de la década de 1960. Sin embargo, las condiciones de los bosques en los estados de los Grandes Lagos (E.U.A.) han experimentado sucesión desde que la región fue talada en los inicios del siglo veinte, y se espera que las poblaciones de aves hayan experimentado cambios concomitantes. Proponemos que se considere al período previo a la colonización euro americana como referencia alternativa para evaluar los cambios en las poblaciones de aves. Evaluamos la cantidad, calidad y distribución del hábitat para reproducción de tres especies de aves de bosque (Dendroica pinus, D. fusca y D. virens) a mediados del siglo XIX e inicios del XX. Construimos modelos de las relaciones entre las aves y el hábitat con base en la literatura y conjuntos de datos de abundancia de aves y los aplicamos a los datos de vegetación ampliamente disponibles. Los registros topográficos de tierras públicas originales representaron las condiciones históricas del hábitat, y una combinación de datos del inventario forestal y de cobertura de suelo representaron las condiciones actuales. Evaluamos la robustez del modelo mediante la comparación de la distribución de hábitat actual con sitios de reproducción de aves registrados en el Wisconsin Breeding Bird Atlas. El modelo mostró poco cambio en la cantidad total de hábitat de Dendroica pinus, mientras que tanto D. fusca como D. virens han experimentado pérdidas sustanciales de hábitat. Para las especies examinadas, la calidad del hábitat se ha degradado desde antes de la colonización y la distribución espacial del hábitat cambió entre ecoregiones, con la expansión del rango acompañando la incursión de bosques en hábitats anteriormente abiertos o el reemplazo de bosques nativos con plantaciones de pinos. Las fuentes de pérdida y degradación de hábitats incluyen la pérdida de coníferas y de árboles grandes. Mediante la utilización de fuentes de datos ampliamente disponibles en un modelo de aptitud de hábitat, nuestro método proporciona un análisis a largo plazo de los cambios en el hábitat de aves y una referencia precolonización para evaluar prioridades de conservación actuales. [source]

An operational model predicting autumn bird migration intensities for flight safety

JOURNAL OF APPLIED ECOLOGY, Issue 4 2007
J. VAN BELLE
Summary 1Forecasting migration intensity can improve flight safety and reduce the operational costs of collisions between aircraft and migrating birds. This is particularly true for military training flights, which can be rescheduled if necessary and often take place at low altitudes and during the night. Migration intensity depends strongly on weather conditions but reported effects of weather differ among studies. It is therefore unclear to what extent existing predictive models can be extrapolated to new situations. 2We used radar measurements of bird densities in the Netherlands to analyse the relationship between weather and nocturnal migration. Using our data, we tested the performance of three regression models that have been developed for other locations in Europe. We developed and validated new models for different combinations of years to test whether regression models can be used to predict migration intensity in independent years. Model performance was assessed by comparing model predictions against benchmark predictions based on measured migration intensity of the previous night and predictions based on a 6-year average trend. We also investigated the effect of the size of the calibration data set on model robustness. 3All models performed better than the benchmarks, but the mismatch between measurements and predictions was large for existing models. Model performance was best for newly developed regression models. The performance of all models was best at intermediate migration intensities. The performance of our models clearly increased with sample size, up to about 90 nocturnal migration measurements. Significant input variables included seasonal migration trend, wind profit, 24-h trend in barometric pressure and rain. 4Synthesis and applications. Migration intensities can be forecast with a regression model based on meteorological data. This and other existing models are only valid locally and cannot be extrapolated to new locations. Model development for new locations requires data sets with representative inter- and intraseasonal variability so that cross-validation can be applied effectively. The Royal Netherlands Air Force currently uses the regression model developed in this study to predict migration intensities 3 days ahead. This improves the reliability of migration intensity warnings and allows rescheduling of training flights if needed. [source]

Textural analysis of contrast-enhanced MR images of the breast

MAGNETIC RESONANCE IN MEDICINE, Issue 1 2003
Peter Gibbs
Abstract Texture analysis was applied to high-resolution, contrast-enhanced (CE) images of the breast to provide a method of lesion discrimination. Significant differences were seen between benign and malignant lesions for a number of textural features, including entropy and sum entropy. Using logistic regression analysis (LRA), a diagnostic accuracy of Az = 0.80 ± 0.07 was obtained with a model requiring only three parameters. By initially dividing the patient data into training and test datasets, reasonable model robustness was also established. On combining features obtained using textural analysis with lesion size, time to maximum enhancement, and patient age, a diagnostic accuracy of Az = 0.92 ± 0.05 was demonstrated. Magn Reson Med 50:92,98, 2003. © 2003 Wiley-Liss, Inc. [source]

I,Varieties of Support and Confirmation of Climate Models

ARISTOTELIAN SOCIETY SUPPLEMENTARY VOLUME, Issue 1 2009
Elisabeth A. Lloyd
Today's climate models are supported in a couple of ways that receive little attention from philosophers or climate scientists. In addition to standard ,model fit', wherein a model's simulation is compared to observational data, there is an additional type of confirmation available through the variety of instances of model fit. When a model performs well at fitting first one variable and then another, the probability of the model under some standard confirmation function, say, likelihood, goes up more than under each individual case of fit alone. Thus, two instances of fit of distinct variables of a global climate model using distinct data sets considered collectively will provide stronger evidence for a model than either one of the instances considered individually. This has consequences for model robustness. Sets of models that produce robust results will, if their assumptions vary enough and they each are observationally sound, provide reasons to endorse common structures found in those models. Finally, independent empirical support for aspects and assumptions of the model provides an additional confirmational virtue for climate models, contrary to what is implied by some current philosophical writing on this topic. [source]

Multiple neural networks modeling techniques in process control: a review

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2009
Zainal Ahmad
Abstract This paper reviews new techniques to improve neural network model robustness for nonlinear process modeling and control. The focus is on multiple neural networks. Single neural networks have been dominating the neural network ,world'. Despite many advantages that have been mentioned in the literature, some problems that can deteriorate neural network performance such as lack of generalization have been bothering researchers. Driven by this, neural network ,world' evolves and converges toward better representations of the modeled functions that can lead to better generalization and manages to sweep away all the glitches that have shadowed neural network applications. This evolution has lead to a new approach in applying neural networks that is called as multiple neural networks. Just recently, multiple neural networks have been broadly used in numerous applications since their performance is literally better than that of those using single neural networks in representing nonlinear systems. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd. [source]