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Indicator choice is a crucial step in biodiversity assessments. Forest inventories have the potential to overcome data deficits for biodiversity monitoring on large spatial scales which is fundamental to reach biodiversity policy targets. Structural diversity indicators were taken from information theory to describe forest spatial heterogeneity. Their indicative value for forest stand variables is largely unknown. This case study explores these indicator–indicandum relationships in a lowland, European beech (Fagus sylvatica) dominated forest in Austria, Central Europe. We employed five indicators as surrogates for structural diversity which is an important part of forest biodiversity i.e., Clark & Evans-, Shannon, Stand Density, Diameter Differentiation Index, and Crown Competition factor. The indicators are evaluated by machine learning, to detect statistic inter-correlation in an indicator set and the relationship to twenty explanatory stand variables and five variable groups on a landscape scale. Using the R packages randomForest, VSURF, and randomForest Explainer, 1555 sample plots are considered in fifteen models. The model outcome is decisively impacted by the type and number of explanatory variables tested. Relationships to interval-scaled, common stand characteristics can be assessed most effectively. Variables of 'stand age & density' are disproportionally indicated by our indicator set while other forest stand characteristics relevant to biodiversity are neglected. Within the indicator set, pronounced inter-correlation is detected. The Shannon Index indicates the overall highest, the Stand Density Index the lowest number of stand characteristics. Machine learning proves to be a useful tool to overcome knowledge gaps and provides additional insights in indicator–indicandum relationships of structural diversity indicators. [ABSTRACT FROM AUTHOR]

The second biannual Alpine Winter Conference on Medicinal and Synthetic Chemistry (short: Alpine Winter Conference) took place January 19-23, 2020, in St. Anton in western Austria. There were roughly 180 attendees from around the globe, making this mid-sized conference particularly conducive to networking and exchanging ideas over the course of four and a half days. This report summarizes the key events and presentations given by researchers working in both industry and academia., (© 2021 Wiley-VCH GmbH.)