Anhalt University of Applied Sciences

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half a century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as kernel density estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian bridge movement models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of appropriately using these two categories of distributions and their estimators. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home ranges estimated using estimators of occurrence distributions are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use estimators of range distributions to quantify home ranges and estimators of occurrence distributions to answer other questions in movement ecology, such as when and where an animal crossed a linear feature, visited a location of interest, or interacted with other animals.

Abstract Nature-based Solutions (NbS) are vital for achieving sustainable urban development. While urban tree planting is one of the most important approaches to enhance NbS, current practices often concentrate on tree quantity or green coverage targets. NbS planning studies are also limited to priority identification, revealing a notable gap in both study and practice. This study presents a tree species composition optimization-based framework for maximizing regional NbS by linking local demands and the effectiveness of tree species through a multi-objective optimization algorithm. A case study was designed in Philadelphia, and the optimal tree species composition was explored on 0.25 km 2 grids. The spatial heterogeneity of local demands and the varying effectiveness of tree species highlight the significance of strategic tree placement. The current tree species composition in Philadelphia is suboptimal for achieving NbS. Future greening efforts should increase the proportion of advantageous species, such as Acer saccharinum , Acer rubrum , and Liquidambar styraciflua . Compared with the baseline, the optimized species composition can provide substantially greater ecosystem services, for instance, about 20% more in stormwater management and up to 80% more in microclimate regulation. Two general planning recommendations are proposed: (1) maximize carbon sequestration capacity in areas with insignificant urban hazards; and (2) strategically place trees with specific advantages in targeted response to specific urban hazards. The developed framework provides guidance for efficient urban greening projects, enabling maximization of NbS under given greening quantity targets.

Abstract Background Temperate grasslands and heathlands undergo degradation due to abandonment. Large grazers play a vital role in the restoration of these habitats, acting as mobile links that connect intact with degraded sites in space and time. Improved knowledge of grazers’ movement patterns and space use is required to understand movement in relation to ecological changes and management plans for optimal restoration success. Methods Our study took place in the former military training area Oranienbaumer Heide, central Germany, where cattle and horses were used to counteract abandonment through grazing. We analyzed a 15-year GPS-tracking dataset with high temporal and spatial resolution to explore the year-round movement behavior, space use, and intraspecific interactions of grazers. We used hidden Markov models to classify behavior, which was analyzed in relation to species, seasons, and distance to water sources. We further analyzed space use in terms of movement distance, diffusion rate and home range size using continuous-time stochastic movement models. Lastly, we quantified intraspecific interactions within cattle and horse populations, especially in relation to the separation and consolidation of management units within our study area. Results We showed that cattle were more selective in choosing resting and grazing areas compared to horses. Grazers’ space use revealed that cattle displayed more extensive movement, with greater distances covered, more exploration, and larger home ranges. Both species presented clear seasonal movement patterns. Thereby, cattle remained closer to water sources than horses, especially during summer. Intraspecific interactions differed between species and likely limited mobile link function in horses, as horse herds remained largely separated after fences between management units were removed, while cattle herds consolidated into a single group. Conclusions Both cattle and horses serve as effective mobile links in ecosystems. However, interspecific differences in grazing behavior, space use, and intraspecific interactions can lead to distinct outcomes in grassland restoration. To harness the complementary strengths of each species, mixed-grazing management should be favored whenever possible to maximize restoration benefits.

ABSTRACT Reliable rainfall estimates are essential for understanding extreme rainfall patterns and supporting climate adaptation in vulnerable areas. In Ethiopia’s Jemma sub-basin, limited surface weather observations challenge accurate characterization of rainfall extremes, necessitating the use of satellite and reanalysis products. We evaluated five rainfall datasets, CHIRPS v2.0, TAMSAT v3.1, ERA5, MERRA v2, and MSWEP v2.8, for their ability to replicate daily rainfall, detect rainfall events, and represent rainfall indices across sub-tropical and temperate agro-ecological zones (AEZs). A point to pixel evaluation approach was used, and statistical metrics including continuous metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling Gupta efficiency (KGE), and correlation coefficient (R), and categorical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias index (FBI) were used to assess performance and assign ranks using a comprehensive rating index (CRI). Results show that MSWEP v2.8 is most reliable for daily rainfall across both AEZs, while CHIRPS v2.0 excels in capturing extreme rainfall in the sub-tropical AEZ. MSWEP v2.8 also performs well in the temperate AEZ but shows limitations for indices like R20mm in sub-tropical AEZ. ERA5 and MERRA v2 exhibit higher biases, lower resolution, and weaker correlations. These findings highlight the importance of selecting rainfall products based on indices and regional conditions. MSWEP v2.8 and CHIRPS v2.0 are recommended for flood and drought forecasting, water management, and agricultural planning. Integrating multiple datasets with local observations and establishing additional meteorological stations can further enhance rainfall assessments and modeling.

Currently, the development of electrochemical energy storage systems plays a key role in meeting the growing demand for electric power. Metal-air batteries (MAB) with high specific energy capacity are considered as promising solutions for use in power plants as backup power sources. One of the main limitations of their widespread implementation is the need to improve the efficiency of anode materials. In this paper, we propose the use of porous aluminum electrodes to improve the performance of MAB. Two types of porous anodes manufactured using different technologies were studied. For the powder aluminum anode, the current density was 20–30 mA/ cm², which is comparable to the performance of a monolithic (standard) anode. At the same time, foam aluminum demonstrated higher current density values of 52–64 mA/cm². An additional advantage of porous anodes is their reduced weight (by 10–30%), which helps to improve the weight and size characteristics of MAB and opens up opportunities for creating more efficient energy systems.

Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential omega-3 polyunsaturated fatty acids (n-3 PUFAs) with well-established health benefits. They occur primarily in marine resources, while their quantitative distribution within the glycerolipidome is rarely analyzed. Therefore, we investigated major commercial sources, including 12 microalgal species, the protist Schizochytrium sp., four fish species, and nine commercial n-3 supplements (fish, krill and Schizochytrium-derived “algal” oils) by high-performance thin-layer chromatography–gas chromatography–mass spectrometry (HPTLC–GC–MS). The class-resolved mapping of EPA and DHA revealed signature lipid profiles across all sources. In microalgae, 60–80% of EPA was localized in glycolipids, whereas in Schizochytrium and fish, >90% of DHA occurred in triacylglycerols. Krill oils exhibited phospholipid-rich profiles with ~70% of phosphatidylcholine-bound DHA. Nutritional indices also highlighted major differences: fish and fish oils showed favorable PUFA-to-saturated FA ratios (>0.45) and hypocholesterolemic-to-hypercholesterolemic ratios (>1), while Schizochytrium-based “algal” oils even surpassed these values. The microalgae Nannochloropsis granulata contained the highest EPA content in biomass form, combined with favorable nutritional indices. Beyond total n-3 content in relation to recommended daily intake values, the lipid-class distribution and nutritional indices should be considered decisive metrics for evaluating the health relevance of n-3 resources in the human diet.

• Performance of new dust mitigation strategy on PV Modules based on natural resuspension of dust. • Compares experimental results from the laboratory with outdoor tests. • Proof of the potential to reduce dust by 91%. • Describing dependencies on environmental conditions such as wind direction and wind speed. • Smart heating as a mitigation strategy leads to a negative energy balance of PV systems. Soiling impairs the energy yield of photovoltaic systems, particularly in arid and semi-arid regions. The primary scientific focus is therefore on the development of efficient cleaning technologies. Complementarily, this work considers a strategy for mitigating soiling through smart heating of photovoltaic modules. The findings suggest that the combination of heated photovoltaic modules with favorable wind conditions can lead to a reduction in dust accumulation. This reduction has the maximum potential to generate an estimated solar energy gain of up to 0.064 kWh/m 2 per day. However, a comprehensive evaluation of laboratory tests and outdoor experiments conducted in Morocco reveals an overall negative energy balance of −0.24 kWh/m 2 per day. The heating system consumed more energy than it mitigated soiling-losses. The study demonstrated that low wind speeds of less than 2.7 m/s and unfavorable north wind directions are unsuitable for generating significant resuspension effects despite heating. Consequently, the economic viability of this technology remains uncertain under real-world conditions. Furthermore, the study emphasizes the limitations of the proposed mitigation strategy and identifies the reasons for the overall negative energy yield.

Generative AI technologies are reshaping everyday environments by enabling multimodal interaction. As their ubiquity and agentic capacities grow, there is a pressing need to understand how these systems reshape human–computer interaction in relational, social, and systemic terms. We introduce a scenario-based design pack for investigating Human–GenAI relations. Grounded in assemblage theory and structured around a three-stage process—Prepare, Make, Reflect—the pack supports the prototyping, analysis, and critical reflection of emergent sociotechnical configurations. We evaluated the pack across three deployments: an ACM workshop (n=22), a multidisciplinary design session (n=20), and a university HCI class (n=260). Participants generated scenarios that surfaced relational issues of power, agency, visibility, and care. We contribute the design pack alongside an exploratory framework to advance relational enquiry into multimodal Human–GenAI relations, support more inclusive and socially responsive GenAI practices, and complement FATE approaches by grounding fairness, accountability, and transparency in lived, multimodal configurations.

Outside of performance-focused domains, research software is typically designed with output in mind rather than runtime efficiency. So, the resulting software consumes more resources (time, hardware) and is less scalable, hindering larger or longitudinal studies without adaptations. In this paper, we report our experiences of iteratively identifying and optimizing performance bottlenecks to enable such analyses in an established research software. Specifically, we applied a top-down strategy to Arcan, an architecture-smell detection tool, to develop a tool (AsTdEA) for tracing architecture smells through software evolution. To identify performance bottlenecks and benchmark our improvements, we used the Qualitas Corpus and a custom dataset. We achieved a reduction in processing time of approx. 98 % and reduced the runtime complexity from almost quadratic to close-to-linear. By sharing our process and insights, we hope to guide researchers in optimizing their research software in the future.