The European Commission Cooperation in Science and Technology (COST) Action FA1203 “SMARTER” aims to make recommendations for the sustainable management of Ambrosia across Europe and for monitoring its efficiency and cost-effectiveness. The goal of the present study is to provide a baseline for spatial and temporal variations in airborne Ambrosia pollen in Europe that can be used for the management and evaluation of this noxious plant. The study covers the full range of Ambrosia artemisiifolia L. distribution over Europe (39°N–60°N; 2°W–45°E). Airborne Ambrosia pollen data for the principal flowering period of Ambrosia (August–September) recorded during a 10-year period (2004–2013) were obtained from 242 monitoring sites. The mean sum of daily average airborne Ambrosia pollen and the number of days that Ambrosia pollen was recorded in the air were analysed. The mean and standard deviation (SD) were calculated regardless of the number of years included in the study period, while trends are based on those time series with 8 or more years of data. Trends were considered significant at p < 0.05. There were few significant trends in the magnitude and frequency of atmospheric Ambrosia pollen (only 8% for the mean sum of daily average Ambrosia pollen concentrations and 14% for the mean number of days Ambrosia pollen were recorded in the air). The direction of any trends varied locally and reflected changes in sources of the pollen, either in size or in distance from the monitoring station. Pollen monitoring is important for providing an early warning of the expansion of this invasive and noxious plant.Electronic supplementary materialThe online version of this article (doi:10.1007/s10453-016-9463-1) contains supplementary material, which is available to authorized users.
The Pannonian region is situated in the Carpathian basin where forests have been used intensively for centuries. The article shows a map and a tabular overview of the forest reserves featured as forests ''left for free development'' of the region, and presents the most important stand structural characteristics of beech, mesophytic and thermophilous deciduous forests surveyed recently. The sampling points of six sites were selected to provide preliminary descriptive statistics according to the main types and abandonment status groups (recently managed, long abandoned and old-growth or primary stands) of these forests. In oldgrowth and primary stands the composition (list and mixture ratio of tree species) and stand structure characteristics [gap class distribution, stem density, distribution of relative crown classes and broad diameter at breast height (at 130 cm) classes, density of thick snags, and the amount of lying dead wood] proved to be similar to other European deciduous natural forests, while the abandoned and recently managed stands indicate that these forests are in a transitional stage towards natural ones.
Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian biogeographical region (PBR). The aim of this study was to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a neural network computation. Monitoring stations with 7-day Hirst-type pollen trap having 10-year long validated data set of ragweed pollen were selected for the study from the PBR. Variables including forecasted meteorological data, pollen data of the previous days and nearby monitoring stations were used as input of the model. We used the multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a datadriven method to forecast the behaviour of complex systems. In our case, it has three layers, one of which is hidden. MLP utilizes a supervised learning technique called backpropagation for training to get better
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