The muscarinic stimulation of phospholipid labeling in hippocampus is independent of its cholinergic input

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23245/1/0000178.pd

Modelling the spatial distribution of tree species with fragmented populations from abundance data

Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species (Quercus suber) and two temperate species (Ilex aquifolium and Taxus baccata). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40-50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surfac

Modelling the spatial distribution of tree species with fragmented populations from abundance data

Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species ( Quercus suber ) and two temperate species ( Ilex aquifolium and Taxus baccata ). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40–50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surface

Produzione di mappe climatiche e bioclimatiche mediante Universal Kriging con deriva esterna: teoria ed esempi per l'Italia

n this paper GIS-based maps of climatic and bioclimatic data for Italy have been obtained by interpolating values observed at measurement stations. Long-term (1961-1990) average monthly data were obtained from weather stations measuring precipitation (1102 sites) and temperature (321 sites). We analysed twelve climatic variables (temperature and precipitation) and nine bioclimatic indexes. Terrain variables and geographical location have been used as predictors of climate variables: longitude, latitude, elevation, aspect, slope, continentality and estimated solar radiation. 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, has been used as spatial interpolator. Based on the root mean square errors from cross-validation tests, we ranked the best search radius for each variable data set. A 15 km search radius has been demonstrated to be the best one to model precipitation variables and precipitation-based bioclimatic indexes, while temperature variables were modelled using a 30 km radius

Predicting the effect of climate change on tree species abundance and distribution at a regional scale

The elaboration of conservation strategies at regional scale, dealing with the potential effects of climate change on the abundance and distribution of tree species, should be supported by models produced at the appropriate scale. We used a bioclimatic model aimed at analysing the large-scale effects of climate change on the abundance and distribution of tree species with respect to their chorological and ecological characteristics. Abundance data for 16 species, sampled in 912 plots, distributed on a 3x3 km grid were used. A climatic model provided high resolution current climatic surfaces and a climatic scenario for 2080 was obtained using the A1FI emission scenario of HadCM3 GCM. A deterministic Regression Tree Analysis (RTA) and Multiple Linear Regression (MLR) were applied in order to define the realised niche of the species in relation to the chosen environmental variables. The comparison between RMSE values showed that RTA always outperforms MLR, in terms of predicting species distribution. Zonal species were better predicted than rare species (extrazonal or with specific habitat requirements). Climate change is expected to determine a general increase of the average potential altitude. Only the Mediterranean species are likely to be favoured by the predicted climate change, while for the two other chorological types (Sub-Mediterranean and Eurosiberian) the response seems to be species-specific, depending on the ecological characteristic of each species: the more thermophilous and xerophilous species should benefit from the predicted drought in terms of area and mean abundance, while mesophilous species should suffer a strong reduction