The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961-2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.18 and 1.68C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

Aim Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. Location Eastern North America (as an example).Methods Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presenceabsence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. ResultsFor both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. Main conclusionsWe conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

. 2005. Potential impacts of climate change on corn, soybeans and barley yields in Atlantic Canada. Can. J. Plant Sci. 85: [345][346][347][348][349][350][351][352][353][354][355][356][357]. In this paper, relationships between agroclimatic indices and average yields of grain corn (Zea mays L.), soybeans (Glycine max L. Merr.) and barley (Hordeum vulgare L.) in field trials conducted in eastern Canada are explored and then used to estimate potential impacts of climate change scenarios on anticipated average yields and total production of these commodities for the Atlantic region for the 2040 to 2069 period. Average yields of grain corn and soybeans were highly correlated (R 2 = 0.86 and 0.74, respectively) with average available crop heat units (CHU), with yields increasing by about 0.006 t ha -1 CHU -1 for corn and 0.0013 t ha -1 CHU -1 for soybeans. The explained variance was not improved significantly when water deficit (DEFICIT) was included as an independent variable in regression. Correlations between average yields of barley and effective growing degree-days (EGDD) were low (R 2 ≤ 0.26) and negative, i.e., there was a tendency for slightly lower yields at higher EGDD values. Including a second-order polynomial for DEFICIT in the regression increased the R 2 to ≥ 0.58, indicating a tendency for lower barley yields in areas with high water deficits and with water surpluses. Based on a range of available heat units projected by multiple General Circulation Model (GCM) experiments, average yields achievable in field trials could increase by about 2.6 to 7.5 t ha -1 (40 to 115%) for corn, and by 0.6 to 1.5 t ha -1 (21 to 50%) for soybeans by 2040 to 2069, not including the direct effect of increased atmospheric CO 2 concentrations, advances in plant breeding and crop production practices or changes in impacts of weeds, insects and diseases on yield. Anticipated reductions in barley yields are likely to be more than offset by the direct effect of increased CO 2 concentrations. As a result of changes in potential yields, there will likely be significant shifts away from production of barley to high-energy and high-protein crops (corn and soybeans) that are better adapted to the warmer climate. However, barley and other small grain cereals will likely remain as important crops as they are very suited for rotation with potatoes. There is a need to evaluate the potential environmental impacts of these possible shifts in crop production, particularly with respect to soil erosion in the region. L'inclusion du déficit hydrique comme variable indépendante à l'analyse de régression n'améliore pas la variance expliquée de manière significative. Le rendement moyen de l'orge est peu corrélé (R 2 _ 0,26) et de manière négative avec le nombre de degrés-jours de croissance (DJC), à savoir le rendement diminue légèrement quand le nombre de DJC augmente. L'inclusion d'un polynôme du deuxième degré pour le déficit hydrique à l'analyse de régression augmente la valeur R 2 à ≥ 0,58, signe que le rendement de l'orge a tendance à êtr...