Home About us Contact
|
Home About us Contact | ||
|
|
||
Comparing Predictions (comparing + prediction)
Selected AbstractsIntraseasonal climate and habitat-specific variability controls the flowering phenology of high alpine plant speciesFUNCTIONAL ECOLOGY, Issue 2 2010Karl Hülber Summary 1. ,High alpine plants endure a cold climate with short growing seasons entailing severe consequences of an improper timing of development. Hence, their flowering phenology is expected to be rigorously controlled by climatic factors. 2. ,We studied ten alpine plant species from habitats with early and late melting snow cover for 2 years and compared the synchronizing effect of temperature sums (TS), time of snowmelt (SM) and photoperiod (PH) on their flowering phenology. Intraseasonal and habitat-specific variation in the impact of these factors was analysed by comparing predictions of time-to-event models using linear mixed-effects models. 3. ,Temperature was the overwhelming trigger of flowering phenology for all species. Its synchronizing effect was strongest at or shortly after flowering indicating the particular importance of phenological control of pollination. To some extent, this pattern masks the common trend of decreasing phenological responses to climatic changes from the beginning to the end of the growing season for lowland species. No carry-over effects were detected. 4. ,As expected, the impact of photoperiod was weaker for snowbed species than for species inhabiting sites with early melting snow cover, while for temperature the reverse pattern was observed. 5. ,Our findings provide strong evidence that alpine plants will respond quickly and directly to increasing temperature without considerable compensation due to photoperiodic control of phenology. [source] Modelling the effects of loss of soil biodiversity on ecosystem functionGLOBAL CHANGE BIOLOGY, Issue 1 2002H. W. Hunt Abstract There are concerns about whether accelerating worldwide loss of biodiversity will adversely affect ecosystem functioning and services such as forage production. Theoretically, the loss of some species or functional groups might be compensated for by changes in abundance of other species or functional groups such that ecosystem processes are unaffected. A simulation model was constructed for carbon and nitrogen transfers among plants and functional groups of microbes and soil fauna. The model was based on extensive information from shortgrass prairie, and employed stabilizing features such as prey refuges and predator switching in the trophic equations. Model parameters were derived either from the literature or were estimated to achieve a good fit between model predictions and data. The model correctly represented (i) the major effects of elevated atmospheric CO2 and plant species on root and shoot biomass, residue pools, microbial biomass and soil inorganic nitrogen, and (ii) the effects on plant growth of manipulating the composition of the microbial and faunal community. The model was evaluated by comparing predictions to data not used in model development. The 15 functional groups of microbes and soil fauna were deleted one at a time and the model was run to steady state. Only six of the 15 deletions led to as much as a 15% change in abundance of a remaining group, and only two deletions (bacteria and saprophytic fungi) led to extinctions of other groups. Functional groups with greater effect on abundance of other groups were those with greater biomass or greater number of consumers, regardless of trophic position. Of the six deletions affecting the abundance of other groups, only three (bacteria, saprophytic fungi, and root-feeding nematodes) caused as much as 10% changes in indices of ecosystem function (nitrogen mineralization and primary production). While the soil fauna as a whole were important for maintenance of plant production, no single faunal group had a significant effect. These results suggest that ecosystems could sustain the loss of some functional groups with little decline in ecosystem services, because of compensatory changes in the abundance of surviving groups. However, this prediction probably depends on the nature of stabilizing mechanisms in the system, and these mechanisms are not fully understood. [source] Validation of simplified PN models for radiative transfer in combustion systemsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 2 2008E. Schneider Abstract This paper illustrates the use of simplified PN approximations as a tools of achieving verification of codes and simulations of radiative transfer in combustion systems. The main advantage of considering these models is the fact that the integro-differential equation for radiative transfer can be replaced by a set of differential equations which are independent of angle variable, compatible to the partial differential equations of flow and combustion, and easy to solve using standard numerical discretizations. Validation of these models is then performed by comparing predictions to measurements for a three-dimensional diffusion flame. The good agreement between measurements and predictions indicates that the simplified PN models can be used to incorporate radiation transfer in combustion systems at very low computational cost without relying on discrete ordinates or Monte Carlo methods. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Lagrangian,Eulerian model of particle dispersion in a turbulent plane mixing layerINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 5 2002L.A. Oliveira Abstract A Lagrangian,Eulerian model for the dispersion of solid particles in a two-dimensional, incompressible, turbulent flow is reported and validated. Prediction of the continuous phase is done by solving an Eulerian model using a control-volume finite element method (CVFEM). A Lagrangian model is also applied, using a Runge,Kutta method to obtain the particle trajectories. The effect of fluid turbulence upon particle dispersion is taken into consideration through a simple stochastic approach. Validation tests are performed by comparing predictions for both phases in a particle-laden, plane mixing layer airflow with corresponding measurements formerly reported by other authors. Even though some limitations are detected in the calculation of particle dispersion, on the whole the validation results are rather successful. Copyright © 2002 John Wiley & Sons, Ltd. [source] Spatial and temporal variation in the relative contribution of density dependence, climate variation and migration to fluctuations in the size of great tit populationsJOURNAL OF ANIMAL ECOLOGY, Issue 2 2009Vidar Grøtan Summary 1The aim of the present study is to model the stochastic variation in the size of five populations of great tit Parus major in the Netherlands, using a combination of individual-based demographic data and time series of population fluctuations. We will examine relative contribution of density-dependent effects, and variation in climate and winter food on local dynamics as well as on number of immigrants. 2Annual changes in population size were strongly affected by temporal variation in number of recruits produced locally as well as by the number of immigrants. The number of individuals recruited from one breeding season to the next was mainly determined by the population size in year t, the beech crop index (BCI) in year t and the temperature during March,April in year t. The number of immigrating females in year t + 1 was also explained by the number of females present in the population in year t, the BCI in autumn year t and the temperature during April,May in year t. 3By comparing predictions of the population model with the recorded number of females, the simultaneous modelling of local recruitment and immigration explained a large proportion of the annual variation in recorded population growth rates. 4Environmental stochasticity especially caused by spring temperature and BCI did in general contribute more to annual fluctuations in population size than density-dependent effects. Similar effects of climate on local recruitment and immigration also caused covariation in temporal fluctuations of immigration and local production of recruits. 5The effects of various variables in explaining fluctuations in population size were not independent, and the combined effect of the variables were generally non-additive. Thus, the effects of variables causing fluctuations in population size should not be considered separately because the total effect will be influenced by covariances among the explanatory variables. 6Our results show that fluctuations in the environment affect local recruitment as well as annual fluctuations in the number of immigrants. This effect of environment on the interchange of individuals among populations is important for predicting effects of global climate change on the pattern of population fluctuations. [source] Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche modelsJOURNAL OF BIOGEOGRAPHY, Issue 12 2009Samuel D. Veloz Abstract Aim, Environmental niche models that utilize presence-only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ,training' and ,testing' purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location, The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods, Two types of niche modelling algorithms , the genetic algorithm for rule-set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) , were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence-only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results, The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions, The findings indicate that care should be taken when interpreting the results from presence-only niche models when training and test data have been randomly partitioned but occurrence data were non-randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions. [source] Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat dataMAMMAL REVIEW, Issue 1 2007GERALDINE NEWTON-CROSS ABSTRACT 1Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology. 2The badger Meles meles is a highly adaptable, medium-sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales. 3The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field-derived and remotely derived data. 4In this paper, we make quantified comparisons between large-scale presence/absence models for badgers in Britain, based on field-surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil. 5We developed four models: 1980s badger survey data using field-based and digital data, and 1990s badger survey data using field-based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it. 6All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field-derived habitat data. 7The high classificatory ability of the digital-based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife-habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife-habitat models should enhance their accuracy, repeatability and applicability and make them better-suited as tools to aid policy- and decision-making processes. [source] Abundances, masses and weak-lensing mass profiles of galaxy clusters as a function of richness and luminosity in ,CDM cosmologiesMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 1 2010Stefan Hilbert ABSTRACT We test the concordance , cold dark matter (,CDM) cosmology by comparing predictions for the mean properties of galaxy clusters to observations. We use high-resolution N -body simulations of cosmic structure formation and semi-analytic models of galaxy formation to compute the abundance, mean density profile and mass of galaxy clusters as a function of richness and luminosity, and we compare these predictions to observations of clusters in the Sloan Digital Sky Survey (SDSS) maxBCG catalogue. We discuss the scatter in the mass,richness relation, the reconstruction of the cluster mass function from the mass,richness relation and fits to the weak-lensing cluster mass profiles. The impact of cosmological parameters on the predictions is investigated by comparing results from galaxy models based on the Millennium Simulation (MS) and the WMAP1 simulation to those from the WMAP3 simulation. We find that the simulated weak-lensing mass profiles and the observed profiles of the SDSS maxBCG clusters agree well in shape and amplitude. The mass,richness relations in the simulations are close to the observed relation, with differences ,30 per cent. The MS and WMAP1 simulations yield cluster abundances similar to those observed, whereas abundances in the WMAP3 simulation are two to three times lower. The differences in cluster abundance, mass and density amplitude between the simulations and the observations can be attributed to differences in the underlying cosmological parameters, in particular the power spectrum normalization ,8. Better agreement between predictions and observations should be reached with a normalization 0.722 < ,8 < 0.9 (probably closer to the upper value), i.e. between the values underlying the two simulation sets. [source] | |||||||||||||