Climatic data and bioclimatic indexes have been used to study plants, animals and ecosystem distribution. GIS‐based maps of climatic and bioclimatic data can be obtained by interpolating values observed at measurement stations. Since no single method can be considered as optimal for all observed regions, a major task is to propose comparisons between results obtained using different methods applied to the same data set of climate variables. We compared three methods that have been proved to be useful at regional scale: 1 ‐ a local interpolation method based on de‐trended inverse distance weighting (D‐IDW), 2 ‐ universal kriging (i.e. simple kriging with trend function defined on the basis of a set of covariates) which is optimal (i.e. BLUP, best linear unbiased predictor) if spatial association is present, 3 ‐ multilayer neural networks trained with backpropagation (representing a complex nonlinear fitting). Long‐term (1955–1990) average monthly data were obtained from weather stations measuring precipitation (201 sites) and temperature (102 sites). We analysed twelve climatic variables (temperature and precipitation) and nine bioclimatic indexes. Terrain variables and geographical location have been used as predictors of the climate variables: longitude, latitude, elevation, aspect, slope, continentality and estimated solar radiation. Based on the root mean square errors from cross‐validation tests, we ranked the best method for each variable data set. Universal kriging with external drift obtained the best performances for seventeen variables of the twenty‐one analysed, neural network interpolator has proven to be more efficient for three variables and D‐IDW for only one. Based on these results, we used the universal kriging estimates to produce the climatic and bioclimatic maps aimed at defining the bioclimatic envelope of species. Copyright © 2007 Royal Meteorological Society

Background: One of the most widely used risk stratification systems for estimating individual patients' risk of persistent or recurrent differentiated thyroid cancer (DTC) is the American Thyroid Association (ATA) guidelines. The 2015 ATA version, which has increased the number of patients considered at low or intermediate risk, has been validated in several retrospective, single-center studies. The aims of this study were to evaluate the real-world performance of the 2015 ATA risk stratification system in predicting the response to treatment 12 months after the initial treatment and to determine the extent to which this performance is affected by the treatment center in which it is used. Methods: A prospective cohort of DTC patients collected by the Italian Thyroid Cancer Observatory web-based database was analyzed. We reviewed all records present in the database and selected consecutive cases that satisfied inclusion criteria: (i) histological diagnosis of DTC, with the exclusion of noninvasive follicular thyroid neoplasm with papillary-like nuclear features; (ii) complete data of the initial treatment and pathological features; and (iii) results of 1-year follow-up visit (6–18 months after the initial treatment), including all data needed to classify the estimated response to treatment. Results: The final cohort was composed of 2071 patients from 40 centers. The ATA risk of persistent/recurrent disease was classified as low in 1109 patients (53.6%), intermediate in 796 (38.4%), and high in 166 (8.0%). Structural incomplete responses were documented in only 86 (4.2%) patients: 1.5% in the low-risk, 5.7% in the intermediate-risk, and 14.5% in the high-risk group. The baseline ATA risk class proved to be a significant predictor of structural persistent disease, both for intermediate-risk (odds ratio [OR] 4.67; 95% confidence interval [CI] 2.59–8.43) and high-risk groups (OR 16.48; CI 7.87–34.5). Individual center did not significantly influence the prediction of the 1-year disease status. Conclusions: The ATA risk stratification system is a reliable predictor of short-term outcomes in patients with DTC in real-world clinical settings characterized by center heterogeneity in terms of size, location, level of care, local management strategies, and resource availability.