Models to predict the distribution and abundance of breeding ducks in Canada

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.

Examination of Relationships between Clear-Sky Longwave Radiation and Aspects of the Atmospheric Hydrological Cycle in Climate Models, Reanalyses, and Observations

Relationships between clear-sky longwave radiation and aspects of the atmospheric hydrological cycle are quantified in models, reanalyses, and observations over the period 1980-2000. The robust sensitivity of clear-sky surface net longwave radiation (SNLc) to column-integrated water vapor (CWV) of 1-1.5 Wm(-2) mm(-1) combined with the positive relationship between CWV and surface temperature (T-s) explains substantial increases in clear-sky longwave radiative cooling of the atmosphere (Q(LWc)) to the surface over the period. Clear-sky outgoing longwave radiation (OLRc) is highly sensitive to changes in aerosol and greenhouse gas concentrations in addition to temperature and humidity. Over tropical ocean regions of mean descent, Q(LWc) increases with T-s at similar to 3.5-5.5 W m(-2) K-1 for reanalyses, estimates derived from satellite data, and models without volcanic forcing included. Increased Q(LWc) with warming across the tropical oceans helps to explain model ensemble mean increases in precipitation of 0.1-0.15 mm day(-1) K-1, which are primarily determined by ascent regions where precipitation increases at the rate expected from the Clausius-Clapeyron equation. The implications for future projections in the atmospheric hydrological cycle are discussed

A review and classification of the existing models of cyanobacteria

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

Phosphorus dynamics and export in streams draining micro-catchments: Development of empirical models

Annual total phosphorus (TP) export data from 108 European micro-catchments were analyzed against descriptive catchment data on climate (runoff), soil types, catchment size, and land use. The best possible empirical model developed included runoff, proportion of agricultural land and catchment size as explanatory variables but with a low explanation of the variance in the dataset (R-2 = 0.37). Improved country specific empirical models could be developed in some cases. The best example was from Norway where an analysis of TP-export data from 12 predominantly agricultural micro-catchments revealed a relationship explaining 96% of the variance in TP-export. The explanatory variables were in this case soil-P status (P-AL), proportion of organic soil, and the export of suspended sediment. Another example is from Denmark where an empirical model was established for the basic annual average TP-export from 24 catchments with percentage sandy soils, percentage organic soils, runoff, and application of phosphorus in fertilizer and animal manure as explanatory variables (R-2 = 0.97).

Predictability and skill of boreal winter forecasts made with the ECMWF Seasonal Forecasting System II

The performance of boreal winter forecasts made with the European Centre for Medium-Range Weather Forecasts (ECMWF) System 11 Seasonal Forecasting System is investigated through analyses of ensemble hindcasts for the period 1987-2001. The predictability, or signal-to-noise ratio, associated with the forecasts, and the forecast skill are examined. On average, forecasts of 500 hPa geopotential height (GPH) have skill in most of the Tropics and in a few regions of the extratropics. There is broad, but not perfect, agreement between regions of high predictability and regions of high skill. However, model errors are also identified, in particular regions where the forecast ensemble spread appears too small. For individual winters the information provided by t-values, a simple measure of the forecast signal-to-noise ratio, is investigated. For 2 m surface air temperature (T2m), highest t-values are found in the Tropics but there is considerable interannual variability, and in the tropical Atlantic and Indian basins this variability is not directly tied to the El Nino Southern Oscillation. For GPH there is also large interannual variability in t-values, but these variations cannot easily be predicted from the strength of the tropical sea-surface-temperature anomalies. It is argued that the t-values for 500 hPa GPH can give valuable insight into the oceanic forcing of the atmosphere that generates predictable signals in the model. Consequently, t-values may be a useful tool for understanding, at a mechanistic level, forecast successes and failures. Lastly, the extent to which t-values are useful as a predictor of forecast skill is investigated. For T2m, t-values provide a useful predictor of forecast skill in both the Tropics and extratropics. Except in the equatorial east Pacific, most of the information in t-values is associated with interannual variability of the ensemble-mean forecast rather than interannual variability of the ensemble spread. For GPH, however, t-values provide a useful predictor of forecast skill only in the tropical Pacific region.

Review of theoretical models of electromagnetic NDT and their applications

There is an increasing interest in modelling electromagnetic methods of NDT - particularly eddy currents. A collaboration within the International Institute of Welding led to a survey intended to explain to non mathematicians the present scope of modelling. The present review commences with this survey and then points out some of the developments and some of the outstanding problems in transferring modelling into industry.

Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar-ethanol-oil nexus in Brazil

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices.

Demonstration models of the cost and benefits of BVD and Johne's contol on cattle farms

Demonstration models of the costs of BVD and Johnes in dairy and beef cattle and the costs and benefits of control have been developed. An example applied to BVD in dairy cattle is presented. Downloadable versions of the models, together with supporting material on how to use them are available to veterinarians from a dedicated website.

Demonstration models of the cost and benefits of BVD and Johne's contol on cattle farms

Demonstration models of the costs of BVD and Johnes in dairy and beef cattle and the costs and benefits of control have been developed. An example applied to BVD in dairy cattle is presented. Downloadable versions of the models, together with supporting material on how to use them are available to veterinarians from a dedicated website.

Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial

The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.