QuestionsMultiple Potential Natural Vegetation (MPNV) is a framework for the probabilistic and multilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional Potential Natural Vegetation (PNV) estimates? Location Hungary MethodsMPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models (GBM). Vegetation data from the Hungarian Actual Habitat Database (MÉTA) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (rescaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. ResultsThe main result of MPNV modelling is a series of raw and rescaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within-type analyses, while rescaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a location's PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. ConclusionThe MPNV approach allows for viewing the potential vegetation composition of locations in far more detail than the PNV approach. Rescaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritisation, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and applications.

1. Bioclimatic variables (BCVs) are routinely used in potential distribution models, typically without considering their calculation options in detail. We aimed at studying the impact of a decision, yet unexamined, on the calculation of BCVs, namely whether the identity of specific months/quarters in the calculation of BCVs should be updated for the future periods (temporal context). Effects on the performance of potential distribution models and on their projections were investigated. Additionally, we also aimed at comparing the impact of month/quarter shifts to that of climate model selection and covariate selection. 2. Potential natural vegetation models encompassing eight habitat types and the whole territory of Hungary were created using boosted regression trees. We tested multiple initial covariate sets to compare the impact of the temporal context to that of covariate selection. The resulting models were applied to the reference and one future time period (with data from two regional climate models). The effect of the BCV calculation approach was tested by linear mixed-effects models and model goodness-of-fit measures in a comprehensive framework of 192 predictions. Area under the ROC curve (AUC) and true positive rate (TPR) curves were used to evaluate the models. 3. Our results show that (a) temporal context of BCVs in interaction with covariate selection had a strong effect on model structure as well as on projections; (b) no evidence supporting the superiority of the widely applied calculation approach of BCVs was found. However, we found notable differences under the two approaches and examples of projection artefacts when applying the widespread way of calculation. 4. We conclude that (a) more attention and more transparent communication is needed when BCVs are used as covariates in distribution models; (b) not only ecophysiology but also the way covariates are calculated should be considered when preselecting covariates for potential distribution models.

The Carpathian Basin is one of the most important regions in terms of the invasion of the common ragweed (Ambrosia artemisiifolia) in Europe. The invasion history of this weed, however, seems to have been assessed differently in Austria and Hungary: scientists in both countries assumed that this species had become abundant earlier and had caused more problems in their own than in other country. The goal of this study is to resolve the historical misunderstandings and scrutinize the related popular beliefs by a concise literature overview and an extensive analysis of the current patterns in ragweed infestations in crops in the borderlands in eastern Austria and western Hungary. The abundance of A. artemisiifolia was measured in 200 arable fields across the region, along with 31 background variables. Data were analysed using binomial generalized linear models (GLM), decision tree models and variation partitioning. Ambrosia artemisiifolia occurred more frequently in Hungary, but there were no significant differences in the proportion of larger cover values recorded in these two countries, and 'cover values > 10%' were even slightly more common in Austria. We found that previous crops of maize and soya bean and conventional farming were associated with the higher abundances in Austria, while organic farming was associated with relatively higher frequencies of heavy infestations in Hungarian fields. In the overall analysis crop cover was the most important variable with low crop cover associated with high ragweed abundance. Temperature and phosphorous fertilizer were negatively, while precipitation and soil phosphorous concentration positively associated with the abundance values. Land-use variables accounted for more of the variance in the abundance patterns of common ragweed than environmental variables. The current patterns in ragweed distribution might indicate that a saturation process is still underway on the Austrian side. The saturation lag of 20-30 years is possibly due to several factors and the role of the Iron Curtain in determining cross-border exchange of propagules could be decisive. Nevertheless, the discrepancies uncovered in the accounts of the invasion of Hungarian and Austrian authors might also be seen as legacies of the Iron Curtain, which were caused by mutual limitations on access to national data and literature of the other country in a critical period of rapid ragweed spread. These discrepancies, that had a long-lasting effect on the work of scientific communities, are documented here in detail for the first time. K e y w o r d s: agriculture, arable fields, common ragweed, invasion, invasive plants, ragweed, spread, weed distribution, weed ecology

Estimating the tick-borne encephalitis (TBE) infection risk under substantial uncertainties of the vector abundance, environmental condition and human-tick interaction is important for evidence-informed public health intervention strategies. Estimating this risk is computationally challenging since the data we observe, i.e., the human incidence of TBE, is only the final outcome of the tick-host transmission and tick-human contact processes. The challenge also increases since the complex TBE virus (TBEV) transmission cycle involves the non-systemic route of transmission between co-feeding ticks. Here, we describe the hidden Markov transition process, using a novel TBEV transmission-human case reporting cascade model that couples the susceptible-infected compartmental model describing the TBEV transmission dynamics among ticks, animal hosts and humans, with the stochastic observation process of human TBE reporting given infection. By fitting human incidence data in Hungary to the transmission model, we estimate key parameters relevant to the tick-host interaction and tick-human transmission. We then use the parametrized cascade model to assess the transmission potential of TBEV in the enzootic cycle with respect to the climate change, and to evaluate the contribution of non-systemic transmission. We show that the TBEV transmission potential in the enzootic cycle has been increasing along with the increased temperature though the TBE human incidence has dropped since 1990s, emphasizing the importance of persistent public health interventions. By demonstrating that non-systemic transmission pathway is a significant factor in the transmission of TBEV in Hungary, we conclude that the risk of TBE infection will be highly underestimated if the non-systemic transmission route is neglected in the risk assessment.