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A citizen science initiative was launched in the province of Trieste, aimed at mapping the distribution of Ailanthus altissima, Ambrosia artemisiifolia and Senecio inaequidens. The reliability of citizen data was tested against control data obtained by trained personnel with a stratified random sampling. In spite of the lack of a strict sampling strategy, citizen data were highly correlated with control data. This was mainly due to: 1) the easy identification of the species and 2) the instructions given to citizens for selecting their observation areas and for avoiding duplicate records. The three species tend to be most frequent in disturbed areas; Ailanthus is highly concentrated in the urban area, Senecio is widely distributed but avoids the city centre, with the highest frequency in the industrial area and along the railways, Ambrosia has a similar pattern, but is most frequent in areas with calcareous substrata. The interpretation of the distribution patterns in terms of land use and ecological factors proved to be quite easy when the three species are considered together (higher frequency in disturbed areas), less so when they are considered separately, most probably because of historical and sociological factors, such as incomplete migration into the survey area, and "gentrification effects".

The effectiveness of outdoor education in comparison to classroom (indoor) education and the acceptance of biology apps by students is still largely unknown. To bridge this gap, a quasi-experiment was performed with secondary school students within each of the schools. Students used a digital dichotomous key to identify woody species. One school performed outdoor identification and the other indoor identification. The students completed a pre-test and post-test on botanical knowledge, and they completed questionnaires about their attitudes toward the key and their attitudes toward biology and learning. The identification of plants with the app proved to be successful in promoting learning regardless of the learning scenario. The comparable results of the indoor and outdoor tasks indicate that the app itself is effective for learning about plants. There were no significant gender-related differences in knowledge results and opinions about the usefulness of biological keys. The key was very well accepted by students and it has proven to be an effective, interesting, and convenient learning tool for identifying organisms that allows experiential learning and learning about biology during the identification process.

Nowadays it is extremely important to understand ecosystem function and its dynamics to predict future changes and consequently to perform appropriate measures. Hydromeliorations and subsequent decrease in groundwater table are thought to be a major reason for a decline in the vitality of black alder (Alnus glutinosa (L.) Gaertn.) wetland forests in North-eastern Slovenia. In this study radial increments of trees were used as indicators of black alder forest function and its disturbances. The aim of the study was to build a model of annual radial increments of black alder trees, to use this model to identify environmental attributes that most importantly affect ecosystem's function and to predict changes in the forest function under different scenarios of environmental conditions in the future. The model was induced with a machine learning algorithm CIPER and it was based on the data about site conditions and applied management measures in the past 35 years. Groundwater levels in combination with the duration of sun radiation were identified as the most important environmental attributes affecting annual radial increments. Radial increments were the lowest in very wet and cloudy years. On the other hand, radial increments were decreased under drought stress as well. Changes in groundwater level and in duration of sun radiation, as well as increased oscillations of groundwater level, all cause important increase in oscillations of modeled radial increments, indicating higher stress. Radial increments were further negatively affected by late white frosts in the spring.

Upgrade of the emergy analysis of Secovlje saltpans [Babic, J., 2005. Emergy analysis of the salt production process at the Secovlje saltpans, Slovenia. In: Proceedings of the Third Biennial Emergy Analysis Research Conference, January 29–31, 2004. The Center for Environmental Policy, Gainesville, FL] was performed with STELLA programming language. STELLA model of the solar salt production process was constructed to simulate emergy flows and emergy indices. Sensitivity analysis of state variables with regard to various emergy indices was also performed. We compared several process scenarios as well as the process behavior under different environmental and economic conditions. The model structure enables adding new state variables in order to obtain better control over emergy flows within the model and between the model and its environment. Results show that evaporated water contributes the highest identified environmental renewable emergy inflow to the area. Precipitations themselves have very negative effect on actual salt production due to the dilution of concentrated brine with rainwater. However, human labor, as it is driven mostly by nonrenewable emergy, contributes the highest nonrenewable emergy inflow. The general trend of decreasing percentage of renewable emergy in human work as well as the need for additional inputs of nonrenewable emergy aimed at reducing labor demand, limits the possibilities to achieve a higher sustainability and a lower environmental loading of the investigated production process. The paper compares the effects of different process modifications and the effect of changing meteorological conditions to the indicators of environmental loading and degree of sustainability.