Abstract
Sowing is a well‐established restoration technique to overcome dispersal limitation. Seed mixtures adapted to certain environmental conditions, like substrate or microclimate, are most ...effective to achieve functional communities. This is especially important if the restored vegetation has to protect critical infrastructure like roadsides and dikes. Here, an improved seed–substrate combination will secure slope stability, make restorations more effective and generate species‐rich grasslands.
A full‐factorial field experiment addressed this topic on a dike at River Danube in SE Germany in 2018–2021. Within 288 plots, we tested three sand admixtures, two substrate depths, two seed densities and two seed mixture types (mesic hay meadow and semidry calcareous grassland) in north and south exposition and measured the recovery completeness by calculating the successional distance to reference sites, the persistence of sown species and the Favourable Conservation Status (FCS) of target species.
Overall, the sown vegetation developed in the desired direction, but a recovery debt remained after 4 years, and some plots still showed similarities to negative references from ruderal sites. In north exposition, hay meadow seed mixtures developed closer to their reference communities than dry grassland mixtures to their reference.
In south exposition, the sown communities established poorly, which might be due to a severe drought during establishment. This initial negative effect remained over the entire observation period.
Sand admixture had a slightly positive effect on target variables, while the tested substrate depths, seed densities and seed mixture types had no effects on species persistence or FCS.
Synthesis and applications
: Site‐adapted seed mixtures make restoration more effective, while applying several seed–substrate combinations might foster beta diversity. Furthermore, additional management efforts are recommended, as they might be necessary to reduce the recovery debt, as well as re‐sowing after unfavourable conditions like droughts.
Zusammenfassung
Die Aussaat ist eine bewährte Renaturierungstechnik da die Samenausbreitung in der Landschaft häufig eingeschränkt ist. Saatgutmischungen, die an ihr Substrat oder ein bestimmtes Mikroklima angepasst sind, sind am effektivsten, um Pflanzengesellschaften erfolgreich zu etablieren. Dies ist besonders wichtig, wenn die etablierte Vegetation Infrastrukturobjekte wie Straßenränder und Deiche schützen soll. In diesem Fall wird eine verbesserte Saatgut‐Substrat‐Kombination die Hangstabilität sichern, die Renaturierung effektiver machen und artenreiches Extensivgrünland hervorbringen.
Ein faktorieller Feldversuch widmete sich diesem Thema auf einem Deich an der Donau in Süddeutschland in den Jahren 2018–2021. Auf 288 Parzellen untersuchten wir drei Sandbeimischungen, zwei Substrattiefen, zwei Saatgutdichten und zwei Saatmischungstypen (Glatthaferwiese, Halbtrockenrasen) in Nord‐ und Südexposition. Den Erfolg der Renaturierung haben wir ermittelt, indem wir folgendes berechneten: den Sukzessionsabstand zu Referenzflächen, die Persistenz der angesäten Arten und den günstigen Erhaltungszustand der Zielarten (Favourable Conservation Status, FCS).
Insgesamt entwickelte sich die eingesäte Vegetation in die gewünschte Richtung, doch blieb nach vier Jahren eine ‘Renaturierungsschuld’ bestehen, und einige Parzellen wiesen immer noch Ähnlichkeiten mit negativen Referenzen von Ruderalstandorten auf. In der Nordexposition entwickelten sich die Glatthaferwiesen‐Saatmischungen näher an den ihre Referenz als die Halbtrockenrasenmischungen.
In der Südexposition etablierten sich die ausgesäten Mischungen schlecht, was auf eine starke Trockenheit während der Etablierung zurückzuführen sein könnte. Dieser anfängliche negative Effekt blieb über den gesamten Beobachtungszeitraum bestehen.
Die Sandbeimischung wirkte sich leicht positiv auf die Zielvariablen aus, während die untersuchten Substrattiefen, Saatdichten und Saatmischungsarten keine Auswirkungen auf die Persistenz der Arten oder den FCS hatten.
Synthese und Anwendungen
: Standortangepasste Saatmischungen machen die Renaturierung effektiver, während die Anwendung verschiedener Saat‐Substrat‐Kombinationen die Beta‐Diversität fördern könnte. Darüber hinaus werden zusätzliche Pflegemaßnahmen empfohlen, da sie notwendig sein könnten, um die ‘Renaturierungsschuld’ zu verringern, sowie die Wiederaussaat nach ungünstigen Bedingungen wie einer Trockenheit.
Aims
Understanding the spatio‐temporal patterns of restoration outcomes is crucial to improve predictability of restoration. High beta diversity of species‐rich communities is sought because it ...increases overall biodiversity and improves ecosystem stability and multifunctionality. For predictive restoration, it is important to identify the significance of drivers like site characteristics but also uncontrolled factors such as spatial effects, historical factors, and year effects.
Location
Dikes at river Danube, SE Germany.
Methods
We studied dike grasslands 4–19 years after restoration over five years (2017–2021, 41 plots in 12 sites). We calculated beta diversity indices to describe spatial variation and temporal turnover, including their additive components ‘replacement’ and ‘nestedness’, or ‘gains’ and ‘losses’. We analysed the main drivers of beta diversity like local site characteristics, landscape, and historical factors.
Results
Spatial variation of the restored dike grasslands was dominated by the replacement component and showed no homogenisation despite a significant temporal turnover. The replacement drivers changed over time, although replacement was mainly affected by slope aspect and landscape factors. Historical factors were inconsistent over time, and no statistically clear drivers of nestedness were found. The dike grasslands exhibited a year‐to‐year turnover in species composition of 37 ± 11%. Gains and losses were balanced over time, although the ratio changed and was most pronounced on south‐facing slopes.
Conclusions
The restored grasslands exhibited spatial variation by site characteristics but also by spatial factors which were not controlled by restorations. Moreover, high non‐directional temporal turnover occurred, caused most likely by weather fluctuations, slightly varying management, and stochastic biotic dynamics. Thus, flexible targets are recommended for restoration monitoring, by defining a set of desired states within a certain range. Furthermore, the dominance of the replacement component of spatial variation should move the focus from defining one precise restoration approach to defining a set of possible methods which together would foster beta diversity.
Understanding spatio‐temporal patterns of restoration outcomes is crucial to improve predictability of restoration. A high beta diversity of species‐rich communities is sought because it increases overall biodiversity. We studied dike grasslands and analysed the main drivers. The grasslands exhibited spatial variation by site characteristics and spatial factors which were not controlled by restorations. Moreover, high non‐directional temporal turnover occurred.
•Cluster analysis of articles based on intra-day sales pattern.•Exploiting of article clusters and organizational structure for demand forecasting.•DSS for ordering fast-moving substitutable ...perishable goods.•Evaluation based on real data of an industrialized bakery.
Demand forecasting is of particular importance for retailers in the context of supply chains of perishable goods and fresh food. Such goods are daily produced and delivered as they need to be provided as fresh as possible and quickly deteriorate. Demand underestimation and overestimation negatively affect the revenues of the retailer. Stock-outs have an undesired impact on consumers while unsold items need to be discarded at the end of the day. We propose a DSS that supports day-to-operations by providing hierarchical forecasts at different organizational levels based on most recent point-of-sales data. It identifies article clusters that are used to extend the hierarchy based on intra-day sales pattern. We apply multivariate ARIMA models to forecast the daily demand to support operational decisions. We evaluate the approach with point-of-sales data of an industrialized bakery chain and show that it is possible to increase the availability while limiting the loss at the same time. The cluster analysis reveals that substitutable items have similar intra-day sales pattern which makes it reasonable to forecast the demand at an aggregated level. The accuracy of top-down forecasts is comparable to direct forecasts which allows reducing the computational costs.
The border is one of the most urgent issues of our times. We tend to think of a border as a static line, but recent bordering techniques have broken away from the map, as governments have developed ...legal tools to limit the rights of migrants before and after they enter a country’s territory. The consequent detachment of state power from any fixed geographical marker has created a new paradigm: the shifting border, an adjustable legal construct untethered in space. This transformation upsets our assumptions about waning sovereignty, while also revealing the limits of the populist push toward border-fortification. At the same time, it presents a tremendous opportunity to rethink states’ responsibilities to migrants. This book proposes a new, functional approach to human mobility and access to membership in a world where borders, like people, have the capacity to move.The border is one of the most urgent issues of our times. We tend to think of a border as a static line, but recent bordering techniques have broken away from the map, as governments have developed legal tools to limit the rights of migrants before and after they enter a country’s territory. The consequent detachment of state power from any fixed geographical marker has created a new paradigm: the shifting border, an adjustable legal construct untethered in space. This transformation upsets our assumptions about waning sovereignty, while also revealing the limits of the populist push toward border-fortification. At the same time, it presents a tremendous opportunity to rethink states’ responsibilities to migrants. This book proposes a new, functional approach to human mobility and access to membership in a world where borders, like people, have the capacity to move.
Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different ...demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry.
In times of a prevailing sense of crisis and disorder in modern politics, there is a growing sentiment that anger, despair or resignation are more appropriate attitudes to navigate the world than ...hope. Political philosophers have long shared this suspicion and shied away from theorising hope more systematically. The aim of this article is to resist this tendency by showing that hope constitutes an integral part of democratic politics in particular. In making this argument I draw on Kant’s conceptualisation of hope as a psychological condition on action under circumstances where the chances of making a difference are dim. Given that the Kantian agent avoids the threat of despair in the pursuit of political goals by placing trust in her fellow citizens, hope has the potential to positively transform democratic practices.
According to Lea Ypi, Kant’s attempt in the first Critique to unify reason via the practical route fails because his notion of purposiveness as design commits him to a dogmatic metaphysics. I ...challenge this claim on two grounds. First, I argue that practical reason does not have an interest in a strong modal connection that guarantees the unity of freedom and nature rather than a weak modal connection that merely affirms the possibility of our ends. Second, I highlight that the epistemic status of practical ideas is one of faith or hope rather than knowledge. Hence, Kant’s attempt to unify reason via the practical route can be reconstructed in a way that is largely in line with the commitments of his critical philosophy.
Retailers that offer perishable items are required to make hundreds of ordering decisions on a daily basis. For certain products, it is even necessary to make intraday decisions in order to increase ...the freshness of the goods while still serving the demand. We present a use case from the bakery domain where a part of the assortment has to be baked during the day as the delivered goods are not ready for sale. Hence, the operational performance depends on the decisions of the store personnel which can be optimized by a decision support system. Our approach to tackle this problem consists of two distinct phases: First, we forecast the hourly demand for each product. Second, the forecasts are input for a scheduling problem whose solution represents the baking plan that is provided to the store personnel. Based on our empirical evaluation, we conclude that forecasting accuracy has the biggest impact on the operational performance. More enhanced prediction methods noticeably outperform the reference methods. In particular, the machine learning based forecasting model significantly outperforms established time series models. If the computed schedules are executed as suggested, the customers can be served with freshly baked goods.
