Filtern
Dokumenttyp
- Wissenschaftlicher Artikel (51) (entfernen)
Schlagworte
- Satellitenfernerkundung (8)
- Fernerkundung (7)
- Modellierung (7)
- Atmosphärische Grenzschicht (5)
- Meereis (5)
- Arctic (4)
- Arktis (4)
- Rheinland-Pfalz (4)
- Bodenerosion (3)
- Höhlensalamander (3)
Institut
- Raum- und Umweltwissenschaften (51) (entfernen)
Leeches can parasitize many vertebrate taxa. In amphibians, leech parasitism often has potential detrimental effects including population decline. Most of studies on the host-parasite interactions involving leeches and amphibians focus on freshwater environments, while they are very scarce for terrestrial amphibians. In this work, we studied the relationship between the leech Batracobdella algira and the European terrestrial salamanders of the genus Hydromantes, identifying environmental features related to the presence of the leeches and their possible effects on the hosts. We performed observation throughout Sardinia (Italy), covering the distribution area of all Hydromantes species endemic to this island. From September 2015 to May 2017, we conducted >150 surveys in 26 underground environments, collecting data on 2629 salamanders and 131 leeches. Water hardness was the only environmental feature correlated with the presence of B. algira, linking this leech to active karstic systems. Leeches were more frequently parasitizing salamanders with large body size. Body Condition Index was not significantly different between parasitized and non-parasitized salamanders. Our study shows the importance of abiotic environmental features for host-parasite interactions, and poses new questions on complex interspecific interactions between this ectoparasite and amphibians.
Production of biomass feedstock for methanation in Europe has focused on silages of maize and cereals. As ecological awareness has increased in the last several years, more attention is being focused on perennial energy crops (PECs). Studies of specific PECs have shown that their cultivation may enhance agrobiodiversity and increase soil organic carbon stocks while simultaneously providing valuable feedstock for methanation. This study was designed to compare soil quality indicators under annual energy crops (AECs), PECs and permanent grassland (PGL) on the landscape level in south-western Germany. At a total 25 study sites, covering a wide range of parent materials, the cropping systems were found adjacent to each other. Stands were commercially managed, and PECs included different species such as the Cup Plant, Tall Wheatgrass, Giant Knotweed, Miscanthus, Virginia Mallow and Reed Canary Grass. Soil sampling was carried out for the upper 20 cm of soil. Several soil quality indicators, including soil organic carbon (Corg), soil microbial biomass (Cmic), and aggregate stability, showed that PECs were intermediate between AEC and PGL systems. At landscape level, mean Corg content for (on average) 6.1-year-old stands of PEC was 22.37 (±7.53) g kg1, compared to 19.23 (±8.08) and 32.08 (±10.11) for AEC and PGL. Cmic contents were higher in PECs (356 ± 241 lgCg1) compared to AECs (291 ± 145) but significantly lower than under PGL (753 ± 417). The aggregate stability increased by almost 65% in PECs compared to AEC but was still 57% lower than in PGL. Indicator differences among cropping systems were more pronounced when inherent differences in the parent material were accounted for in the comparisons. Overall, these results suggest that the cultivation of PECs has positive effects on soil quality indicators. Thus, PECs may offer potential to make the production of biomass feedstock more sustainable.
In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research, especially on soil health, where risk factors like erosion and compaction depend on variables that are specific to every site, and hard to generalize for larger administrative aggregates. The focus of this study is to apply established classification algorithms to estimate maize abundance for each independent pixel, while at the same time accounting for their spatial relationship. Therefore, two ways to incorporate spatial autocorrelation of neighboring pixels are combined with three different classification models. The performance of each of these modeling approaches is analyzed and discussed. Finally, one prediction approach is applied to the imagery, and the overall predicted acreage is compared to publicly available data. We were able to show that Support Vector Machine (SVM) classification and Random Forests (RF) were able to distinguish maize pixels reliably, with kappa values well above 0.9 in most cases. The Generalized Linear Model (GLM) performed substantially worse. Furthermore, Regression Kriging (RK) as an approach to integrate spatial autocorrelation into the prediction model is not suitable in use cases with millions of sparsely clustered training pixels. Gaussian Blur is able to improve predictions slightly in these cases, but it is possible that this is only because it smoothes out impurities of the reference data. The overall prediction with RF classification combined with Gaussian Blur performed well, with out of bag error rates of 0.5% in 2009 and 1.3% in 2016. Despite the low error rates, there is a discrepancy between the predicted acreage and the official records, which is 20% in 2009 and 27% in 2016.
Harvesting of silage maize in late autumn on waterlogged soils may result in several ecological problems such as soil compaction and may subsequently be a major threat to soil fertility in Europe. It was hypothesized that perennial energy crops might reduce the vulnerability for soil compaction through earlier harvest dates and improved soil stability. However, the performance of such crops to be grown on soil that are periodically waterlogged and implications for soil chemical and microbial properties are currently an open issue. Within the framework of a two-year pot experiment we investigated the potential of the cup plant (Silphium perfoliatum L.), Jerusalem artichoke (Helianthus tuberosus), giant knotweed (Fallopia japonicum X bohemica), tall wheatgrass (Agropyron elongatum), and reed canary grass (Phalaris arundinacea) for cultivation under periodically waterlogged soil conditions during the winter half year and implications for soil chemical and biological properties. Examined perennial energy crops coped with periodical waterlogging and showed yields 50% to 150% higher than in the control which was never faced with waterlogging. Root formation was similar in waterlogged and non-waterlogged soil layers. Soil chemical and microbial properties clearly responded to different soil moisture treatments. For example, dehydrogenase activity was two to four times higher in the periodically waterlogged treatment compared to the control. Despite waterlogging, aerobic microbial activity was significantly elevated indicating morphological and metabolic adaptation of the perennial crops to withstand waterlogged conditions. Thus, our results reveal first evidence of a site-adapted biomass production on periodical waterlogged soils through the cultivation of perennial energy crops and for intense plant microbe interactions.
Abstract: Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.
Background
In light of the current biodiversity crisis, DNA barcoding is developing into an essential tool to quantify state shifts in global ecosystems. Current barcoding protocols often rely on short amplicon sequences, which yield accurate identification of biological entities in a community but provide limited phylogenetic resolution across broad taxonomic scales. However, the phylogenetic structure of communities is an essential component of biodiversity. Consequently, a barcoding approach is required that unites robust taxonomic assignment power and high phylogenetic utility. A possible solution is offered by sequencing long ribosomal DNA (rDNA) amplicons on the MinION platform (Oxford Nanopore Technologies).
Findings
Using a dataset of various animal and plant species, with a focus on arthropods, we assemble a pipeline for long rDNA barcode analysis and introduce a new software (MiniBar) to demultiplex dual indexed Nanopore reads. We find excellent phylogenetic and taxonomic resolution offered by long rDNA sequences across broad taxonomic scales. We highlight the simplicity of our approach by field barcoding with a miniaturized, mobile laboratory in a remote rainforest. We also test the utility of long rDNA amplicons for analysis of community diversity through metabarcoding and find that they recover highly skewed diversity estimates.
Conclusions
Sequencing dual indexed, long rDNA amplicons on the MinION platform is a straightforward, cost-effective, portable, and universal approach for eukaryote DNA barcoding. Although bulk community analyses using long-amplicon approaches may introduce biases, the long rDNA amplicons approach signifies a powerful tool for enabling the accurate recovery of taxonomic and phylogenetic diversity across biological communities.
For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI)orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality.
A satellite-based climatology of wind-induced surface temperature anomalies for the Antarctic
(2019)
It is well-known that katabatic winds can be detected as warm signatures in the surface temperature over the slopes of the Antarctic ice sheets. For appropriate synoptic forcing and/or topographic channeling, katabatic surges occur, which result in warm signatures also over adjacent ice shelves. Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used to detect warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for the years of 2002 to 2016. We present a case study and a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological signal with a mean warm anomaly of more than 5K on more than 120 days per winter for the Byrd Glacier and the Nimrod Glacier on the Ross Ice Shelf. The mean anomaly for the Brunt Ice Shelf is weaker, and exceeds 5K on about 70 days per winter. Model simulations of the IST are compared to the MODIS IST, and show a very good agreement. The model data show that the near-surface stability is a better measure for the response to the wind than the IST itself.
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation.
The study analyzes the long-term trends (1998–2019) of concentrations of the air pollutants ozone (O3) and nitrogen oxides (NOx) as well as meteorological conditions at forest sites in German midrange mountains to evaluate changes in O3 uptake conditions for trees over time at a plot scale. O3 concentrations did not show significant trends over the course of 22 years, unlike NO2 and NO, whose concentrations decreased significantly since the end of the 1990s. Temporal analyses of meteorological parameters found increasing global radiation at all sites and decreasing precipitation, vapor pressure deficit (VPD), and wind speed at most sites (temperature did not show any trend). A principal component analysis revealed strong correlations between O3 concentrations and global radiation, VPD, and temperature. Examination of the atmospheric water balance, a key parameter for O3 uptake, identified some unusually hot and dry years (2003, 2011, 2018, and 2019). With the help of a soil water model, periods of plant water stress were detected. These periods were often in synchrony with periods of elevated daytime O3 concentrations and usually occurred in mid and late summer, but occasionally also in spring and early summer. This suggests that drought protects forests against O3 uptake and that, in humid years with moderate O3 concentrations, the O3 flux was higher than in dry years with higher O3 concentrations.