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A growing number of studies have highlighted the consequences of climate change on agriculture, including the impacts of climate extremes such as drought, heat waves and frost. The aim of this study was to assess the influence of temperature extremes on various phenological events of grapevine varieties in Southwest Switzerland (Leytron, Canton of Valais). We aimed to capture the occurrence of extreme events in specific years in various grapevine varieties and at different phenological phases to rank the varieties based on their sensitivity to temperature extremes and thus quantify their robustness. Phenological observations (1978–2018) of six Vitis vinifera varieties (Arvine, Chardonnay, Chasselas, Gamay, Pinot noir and Syrah) were subjected to event coincidence analysis. Extreme events were defined as values in the uppermost or lowermost percentiles of the timing of the phenophases and daily temperatures within a 30-day window before the phenophase event occurred. Significantly more extreme temperature and phenological events occurred in Leytron between 2003 and 2017 than in the earlier years, with the years 2007, 2011, 2014 and 2017 being remarkable in terms of the number of extreme coincidence events. Moreover, bud development and flowering experienced significantly more extreme coincidence events than other phenophases; however, the occurrence rate of extreme coincidence events was independent of the phenophase. Based on the total number of extreme events, the varieties did not differ in their responses to temperature extremes. Therefore, event coincidence analysis is an appropriate tool to quantify the occurrence of extreme events. The occurrence of extreme temperature events clearly affected the advancement of the timings of phenological events in various grapevines. However, there were no varietal differences in terms of response to extreme temperatures; thus, additional research is warranted to outline the best adaptation measures.

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.

In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.

Bees are the most important group of flower visitors providing an essential ecosystem service, namely pollination. Due to the worldwide decline of bees, there should be standardized sampling methods in place to ensure consistent and comparable results between studies. We compared the two commonly used sampling methods of yellow pan traps and transect walk to determine (i) which habitat variables affect the species composition, abundance and species richness of sampled bee communities, (ii) which method potentially contains sampling bias towards some individuals or groups of bees and (iii) the efficiency of sampling in various habitats. We conducted fieldwork in different agricultural habitats distributed along landscape heterogeneity and topography gradients. Our results showed that the height of vegetation, the average number of flowers and the amount of woody vegetation had the greatest influence on the sampling efficiency. Our survey also demonstrated that sampling by transect walk captured less bees in general, especially in stubble, maize, and cereal fields. We found that Apis mellifera and Bombus spp. were well represented in samples collected by the transect walk method, while the abundance of other genera, especially Dasypoda, Hylaeus and Panurgus was higher in pan traps. Based on the results, we suggest (i) the transect walk method to compare samples of flower-visiting wild bee communities from various habitats of different vegetation and flower characteristics, (ii) application of the transect walk or pan traps to compare similar habitats and (iii) adoption of a comprehensive method which would incorporate both sampling techniques to gain a more complex insight into wild bee species composition.

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch), It is known from many studies that plant species show a delay in the timing of flowering events with an increase in latitude and altitude, and an advance with an increase in temperature. Furthermore, in many locations and for many species, flowering dates have advanced over the long-term. New insights using survival modeling are given based on data collected (1970-2010) along a 3000-km long transect from northern to eastern central Europe. We could clearly observe that in the case of dandelion (Taraxacum officinale) the risk of flowering time, in other words the probability that flowering occurs, is higher for an earlier day of year in later decades. Our approach assume that temperature has greater influence than precipitation on the timing of flowering. Evaluation of the predictive power of tested models suggests that Cox models may be used in plant phenological research. The applied Cox model provides improved predictions of flowering dates compared to traditional regression methods and gives further insights into drivers of phenological events.

Long-term changes of plant phenological phases determined by complex interactions of environmental factors are in the focus of recent climate impact research. There is a lack of studies on the comparison of biogeographical regions in Europe in terms of plant responses to climate. We examined the flowering phenology of plant species to identify the spatio-temporal patterns in their responses to environmental variables over the period 1970-2010. Data were collected from 12 countries along a 3000-km-long, North-South transect from northern to eastern Central Europe.Biogeographical regions of Europe were covered from Finland to Macedonia. Robust statistical methods were used to determine the most influential factors driving the changes of the beginning of flowering dates. Significant species-specific advancements in plant flowering onsets within the Continental (3 to 8.3 days), Alpine (2 to 3.8 days) and by highest magnitude in the Boreal biogeographical regions (2.2 to 9.6 days per decades) were found, while less pronounced responses were detected in the Pannonian and Mediterranean regions. While most of the other studies only use mean temperature in the models, we show that also the distribution of minimum and maximum temperatures are reasonable to consider as explanatory variable. Not just local (e.g. temperature) but large scale (e.g. North Atlantic Oscillation) climate factors, as well as altitude and latitude play significant role in the timing of flowering across biogeographical regions of Europe. Our analysis gave evidences that species show a delay in the timing of flowering with an increase in latitude (between the geographical coordinates of 40.9 and 67.9), and an advance with changing climate. The woody species (black locust and small-leaved lime) showed stronger advancements in their timing of flowering than the herbaceous species (dandelion, lily of the valley). In later decades (1991-2010), more pronounced phenological change was detected than during the