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Institute
- Fakultät AuL (270) (remove)
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Recording of Low-Oxygen Stress Response Using Chlorophyll Fluorescence Kinetics in Apple Fruit
(2023)
Long-term storage of apples (Malus x domestica, Borkh.) is increasingly taking place under Dynamic Controlled Atmosphere (DCA). The oxygen level is lowered to ≤ 1 kPa O2 and the apples are stored just above the Lower Oxygen Limit (LOL). Low oxygen stress during controlled atmosphere storage can lead to fermentation in apples if oxygen levels are too low. Chlorophyll fluorescence can be used to detect low-oxygen stress at an early stage during storage. The currently available non-imaging fluorescence systems often use the minimal fluorescence (Fo) parameter. In contrast, the use of chlorophyll fluorescence kinetics is insufficiently described. Therefore, this study aimed to gain more knowledge about the response of chlorophyll fluorescence kinetics to low oxygen stress in apples using a fluorescence imaging system. The results show that the kinetic fluorescence curves differ under aerobic and fermentation conditions. The fermentative conditions initiated a decrease in fluorescence intensity upon application of the saturation pulses during exposure to actinic light. This result was made at 18 °C and 2 °C ambient temperatures. Interestingly, the kinetic curve changed at 2 °C before fermentation products accumulated in the apples. Non-photochemical quenching (NPQ) decreased under fermentation conditions in the dark phase after relaxation. Upon entering the dark relaxation phase after Kautsky induction, ɸPSII began to increase. Under atmospheric oxygen conditions, ɸPSII reached values of 0.81 to 0.76, while under fermentation, ɸPSII values ranged from 0.57 to 0.44.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
SimBO is a flexible framework for optimizing discrete event-driven simulations (DES) using sequential optimization algorithms. While specifically designed for Bayesian Optimization (BO) in the context of DES, SimBO can be applied to any black-box problem with other optimization algorithms. The framework consists of four encapsulated components - the black-box problem, the sequential optimization algorithm, a database for experiment configuration and results, and a web-based graphical user interface - that communicate via well-defined interfaces. Each component can be run in different environments, allowing for cooperation between different hardware- and software configurations. In our research context, SimBO’s architecture enabled BO algorithms to be run on a high-performance cluster with GPU support, while the simulation is executed on a local Windows machine using the Simio simulation software. The framework’s flexibility also makes it suitable for evolving from a research-focused tool to a production-ready, cloud-based optimization tool for modern algorithms.
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.
Farmland bird populations are in a deep crisis across Europe. Agri-environment schemes (AES) were implemented by the European Union to stop and reverse the general decline of biodiversity in agricultural landscapes. In Germany, flower strips are one of the most common AES. Establishing high-quality perennial wildflower strips (WFS) with species-rich native forb mixtures from regional seed propagation is a recent approach, for which the effectiveness for birds has not yet been sufficiently studied. We surveyed breeding birds and vegetation on 40 arable fields with WFS (20 with single and 20 with aggregated WFS) and 20 arable fields lacking WFS as controls across Saxony-Anhalt (Germany). Additionally, vegetation composition, WFS quantity and landscape structure (e.g. distance to nearest woody element) were considered in our analyses. All WFS were established with species-rich native seed mixtures (30 forbs) in agricultural practice as AES. Arable fields with WFS had a higher species richness and territory density of birds than controls, confirming the effectiveness of this AES. A forb-rich vegetation was the main driver promoting birds. Flower strip quantity at the landscape level had positive effects only on bird densities, but also single WFS achieved benefits. A short distance from WFS to woody elements increased total bird species richness. However, the density of farmland birds, which are target species of these AES, were negatively affected by the proximity and proportion of woody elements in the vicinity. The effect of the proportion of non-intensively used open habitats and overall habitat richness was unexpectedly low in the otherwise intensively farmed landscape. Species-rich perennial WFS significantly promoted breeding birds. Successful establishment of WFS, resulting in high-quality habitats, a high flower strip quantity as well as implementation in open landscapes were shown to maximise the effectiveness for restoring declining and AES target farmland birds.
Green roofs can mitigate negative environmental effects of urban densifcation to some extent, but they are often covered by species-poor Sedum mixtures with a low value for biodiversity. By combining a habitat template and a seedprovenance approach, we review the suitability of plant species from regionally occurring dry sandy grasslands (Koelerio-Corynophoretea) for extensive roof greening in northwestern Germany. Since 2015, we have studied the effects of species introduction on vegetation dynamics on experimental mini-roofs. Treatments included sowing seeds of regional native origin in two densities (1 g and 2 g/m2) and the transfer of raked material from an ancient dry grassland area classifed as Natura 2000 site. The applied raked material contained diaspores of 27 vascular plant species (including seven threatened species) and vegetative fragments of grasslandspecifc mosses and lichens. Since 2018, we have tested more species-rich seed mixtures in a large-scale experiment on a roof of 500 m2 with different engineered green-roof substrates and layering. In 2019, a green roof of 10,200 m2 was established in cooperation with a local enterprise to support regional native biodiversity.
In this chapter, we summarise the most important results of our studies and discuss how to support regional native biodiversity on green roofs.
1. Flower strips are a fundamental part of agri-environment schemes (AESs) introduced by the European Union to counteract the loss of biodiversity and related ecosystem services in agricultural landscapes. Although vegetation composition of the strips is essential for most fauna groups, comprehensive studies analysing vegetation development and influencing factors are rare.
2. From 2017 to 2019, we investigated the vegetation composition of 40 perennial wildflower strips (WFSs) implemented in 2015 or 2016, and 20 cereal fields without WFS across Saxony-Anhalt, Germany. We analysed environmental factors on plot (cover of grasses, shading, soil fertility) and four landscape-scale levels (habitat diversity, proportion of WFS and open habitats). The provision of nectar and pollen resources was estimated by the newly developed Pollinator Feeding Index (PFI). All strips had been implemented by farmers as AES with species- rich seed mixtures comprising 30 native forbs.
3. In all study years, forb species richness, cover and related nectar and pollen supply were much higher on WFSs than on controls, confirming the effectiveness of this AES. Although sown native forbs contributed the most to the high PFI values, spontaneously established forbs expanded the total range of species considerably, especially in winter and spring. While sown forb communities remained similar over time, spontaneous forbs showed a higher species turnover. Altogether, shading and grass cover had the greatest negative effect on the performance of the sown forbs. Landscape variables had only minor effects and were inconsistent in their importance across scale levels and years.
4. Synthesis and applications. Successfully established perennial wildflower strips (WFSs) sown with species-rich native seed mixtures provided a forb-rich and diverse vegetation throughout the AES funding period of 5 years. By supplying feeding resources for pollinators under various landscape situations, WFSs have significant potential to promote farmland biodiversity and related ecosyste services. We recommend the mandatory use of species-rich wildflower mixtures for perennial flower strips and to avoid their creation in heavily shaded field edges. Advisory services for farmers are necessary to prevent failures in WFS implementation and management and to improve their ecological effectiveness.
Green roofs are known to mitigate the negative effects of urban consolidation by offering diverse ecosystem functions compared to non-vegetated roofs. However, the support for native biodiversity might be improved by using native plant species. In a mesocosm experiment, we studied the suitability of three commercial green-roof growth substrates for the establishment of 27 native plant species from dry sandy grasslands of northwestern Germany over the course of four years. The substrates were mineral-based, but differed in the layering of organic matter. Total establishment rates reached 44–59% in Year 4, indicating the general suitability of the substrates. During the first weeks after seeding, with light irrigation, the vascular plant cover was greater in the similar substrates Zincolit® Plus (Z) and Zincolit® Plus-Leicht (ZL) with their compost-based organic mulch layers than in the substrate Sedumteppich (ST) with its organic matter evenly admixed with the mineral aggregates. In Years 2 and 3, however, the vascular plant cover was greater in the ST substrate, likely due to the better availability of water and nutrients from the organic matter compared to the dry surface-mulch layer variants Z and ZL. After severe drought events, the decline in plant cover was more pronounced in the ST substrate, likely representing a trade-off between lush growth and a susceptibility to drought. An indicator-species analysis revealed differences in species composition between the ST and Z/ZL substrates. Annual plant species were indicators of the ST substrate. Perennials, such as Thymus pulegioides and Achillea millefolium, were typical of the Z and ZL substrates. In addition to the general suitability of the tested standard substrates for target species establishment, the study indicated that a combination of different layers of substrate components resulted in different vegetation patterns that may have a positive effect on green-roof biodiversity.
Are natural floods accelerators for streambank vegetationdevelopment in floodplain restoration?
(2021)
Riverbanks are very dynamic habitats for riparian vegetation strongly influenced byfluvial and geomorphic processes. This habitat type was severely reduced in the pastby river straightening and bank stabilisation. Restoration and establishment of newfloodplain streams promote this habitat, but a directed succession to later stages wasobserved many times. Our study aimed to analyse whether the often observeddirected succession of the streambank vegetation after restoration implementationcould be reversed by a natural flood along a newly created floodplain stream. Weinvestigated the effects of a natural flood in 2013 and different prerestorationconditions on species development in the riparian zone. Vegetation was studiedalong 12 transects in four different sections from 2011 to 2014. Species composi-tion differed strongly between the sections. Species richness was lowest in a newlydug steep section with high morphological dynamics and highest on wider flatstreambanks. Changes during the years reflecting different hydrological eventsvaried between sections. The high natural flood in 2013 reduced the cover of theherb layer and increased bare ground, which led in most sections to a loss of non-target species. Total target species richness did not change due to the natural flood,while target species showed a high turnover rate. In the following year, however,the flood‐induced development of species composition, in general, was reversed.Natural floods changed abiotic and biotic conditions along the streambank, but theydid not accelerate ecological restoration towards predefined target ecosystems.However, they were necessary to preserve the needed dynamic vegetation changesand species turnover to hinder the succession to later stages dominated by a fewspecies. Our study shows that riparian vegetation near the streambank can bemonitored most effectively in cross‐profile transects, both in the long‐term andevent‐related.