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Background: Only a few decades ago, colorful, small‐scale, heterogeneous, and species‐rich hay meadows or extensive pastures were common, but have often been replaced by species‐poor, uniform, large‐scale multicut meadows. Technological advancements and improved efficiency in grassland management have come at the cost of biodiversity. Methods: In Germany, 150 grassland plots have been investigated since 2006. Using these extensive data, we propose a new compound index for estimating the site‐specific mowing intensity in order to facilitate assessment of the impact of mowing intensity on biodiversity and ecosystem processes. Our index integrates the various qualitative components of mowing machine type, mowing height and use of a conditioner, with the annual number of cuts. Results: The newly proposed index achieves a much finer gradation of mowing intensity compared to the previous quantification based on the number of cuts only. Furthermore, a decrease in plant and arthropod species was observed at higher mowing intensity. Conclusions: The proposed mowing intensity index offers enhanced precision in calculations and can easily be integrated into assessments of land‐use intensity in grasslands. Further, it could serve as a basis for providing subsidies to farmers, who adopt low‐impact mowing practices. [ABSTRACT FROM AUTHOR]

Assembly Theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of Assembly Theory by introducing a simplified Toy Model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of Assembly Theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that this presence of autocatalysis tends to favor longer consecutive construction sequences in environments with a low probability of catalysis, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures. [ABSTRACT FROM AUTHOR]

Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches. [ABSTRACT FROM AUTHOR]