Korallenriffe werden zunehmend mechanisch gestoert; die Bedeckung mit lebenden kalkabscheidenden Steinkorallen ist vielerorts unter die kritische Rate fuer das Riffwachstum gesunken. Zur Rehabilitation geschaedigter Korallenriffe wird in situ geeignetes Besiedlungssubstrat via Elektrolyses aus dem Meerwasser abgeschieden. Neben der Schaffung optimaler Ansiedlungsbedingungen fuer Korallenlarven sorgt eine zusaetzliche 'Aufforstung' mit lebenden Korallenfragmenten fuer die rasche Etablierung von Geruestbildnern. So koennen in veroedeten Arealen Trittsteinbiotope fuer die weitere Besiedlung und grossflaechige Bestandserholung geschaffen werden. Die Methode wurde im Kubikmetermassstab bei Aqaba im Roten Meer erfolgreich erprobt. Spezielle Anlagen fuer die Marikultur von Schwaemmen, Korallen und anderen wirtschaftlich interessanten Arten sowie der Einsatz bei der Schaffung spezieller Erlebnis- und Uebungsraeume fuer Tauchtouristen sind geplant.
Low-lying coral reef islands harbour a distinct, yet highly threatened biological and cultural diversity that is increasingly exposed to climate change impacts. The combination of low elevation, small size, sensitivity to changes in boundary conditions (sea level, waves and currents, locally generated sediment supply) and at some locations high population densities, is why low-lying reef islands (LRIs) are considered among the most vulnerable environments on Earth to climate change. To date, their global distribution and influence of climatic, oceanographic, and geologic setting are only poorly documented or restricted to smaller scales. Here, I present the first detailed global analysis of LRIs utilising freely available global datasets to produce a global reef island database (GRID) and associated intrinsic and extrinsic characteristics that can be used within a coastal vulnerability index (CVI). All datasets used to create the GRID were released between 30 November 2015 and 3 August 2023, while the current version (version 1.1) of the GRID database was completed in August 2026. When developing the GRID, LRIs are defined as landmasses <30 km² located on or within 3 km of coral reef and with an elevation of <16 m. Development of the GRID required: 1) the creation of a global shoreline vector file containing the geographic distribution of LRIs and 2) the development of a comprehensive global database of LRIs including eight intrinsic and ten extrinsic variables extracted from global datasets. Intrinsic variables include: 1) human populations, 2) island area, 3) island perimeter, 4) mean elevation, 5) island circularity/shape, 6) underlying reef type, 7) geographic isolation and 8) distance to the nearest neighbouring reef island. Extrinsic variables include: 1) mean water depth, 2) standard deviation of mean water depth, 3) mean annual significant wave height, 4) mean annual wave period, 5) mean spring tidal range, 6) relative tidal range, 7) wave-tide regime, 8) relative wave exposure, 9) relative tropical storm exposure and 10) year-2100 projected median sea level rise rate. The GRID was initially derived from version 2.1 of the UNEP-WCMC Global Island Database, a global shoreline vector file based on geometry data from Open Street Map® (OSM) and released in November 2015. The initial vector file was projected using the Mollweide projection, an equal-area pseudo cylindrical map projection chosen for its accurate derivation of area, especially in regions close to the equator, where most LRIs are located. The final GRID contains 33,292 individual LRIs distributed throughout tropical regions of the world's oceans, amassing a total land area of nearly 11,000 km² with approximately 60,500 km of shoreline and housing around 2.6 million people. While intrinsic variables are typically spatially homogenous, LRIs are generally highly spatially clustered throughout the GRID with respect to extrinsic variables. The spatial distribution of LRIs within the GRID was validated using: 1) published data and 2) quantitative accuracy assessments using satellite imagery. Spatial distributions of LRIs captured in the GRID are extremely consistent with those published in the literature (r² = 0.96) and those derived from independent analysis of satellite imagery (r² = 0.94). Finally, the GRID was used to develop an island vulnerability index (IVI) for each LRI on a scale of 0-1 with 0 representing no vulnerability and 1 representing maximum vulnerability. The GRID database is provided as a tab-delimited text file as well as ESRI shapefiles (points and polygons in WGS84 and Mollweide projection) and a comma-separated value file.
Low-lying coral reef islands harbour a distinct, yet highly threatened biological and cultural diversity that is increasingly exposed to climate change impacts. The combination of low elevation, small size, sensitivity to changes in boundary conditions (sea level, waves and currents, locally generated sediment supply) and at some locations high population densities, is why low-lying reef islands (LRIs) are considered among the most vulnerable environments on Earth to climate change. To date, their global distribution and influence of climatic, oceanographic, and geologic setting are only poorly documented or restricted to smaller scales. Here, I present the first detailed global analysis of LRIs utilising freely available global datasets to produce a global reef island database (GRID) and associated intrinsic and extrinsic characteristics that can be used within a coastal vulnerability index (CVI). All datasets used to create the GRID were released between 30 November 2015 and 3 August 2023, while the current version of the GRID database was completed in November 2024. When developing the GRID, LRIs are defined as landmasses <30 km² located on or within 1 km of coral reef and with an elevation of <16 m. Development of the GRID required: 1) the creation of a global shoreline vector file containing the geographic distribution of LRIs and 2) the development of a comprehensive global database of LRIs including eight intrinsic and ten extrinsic variables extracted from global datasets. Intrinsic variables include: 1) human populations, 2) island area, 3) island perimeter, 4) mean elevation, 5) island circularity/shape, 6) underlying reef type, 7) geographic isolation and 8) distance to the nearest neighbouring reef island. Extrinsic variables include: 1) mean water depth, 2) standard deviation of mean water depth, 3) mean annual significant wave height, 4) mean annual wave period, 5) mean spring tidal range, 6) relative tidal range, 7) wave-tide regime, 8) relative wave exposure, 9) relative tropical storm exposure and 10) year-2100 projected median sea level rise rate. The GRID was initially derived from version 2.1 of the UNEP-WCMC Global Island Database, a global shoreline vector file based on geometry data from Open Street Map® (OSM) and released in November 2015. The initial vector file was projected using the Mollweide projection, an equal-area pseudo cylindrical map projection chosen for its accurate derivation of area, especially in regions close to the equator, where most LRIs are located. The final GRID contains 34,404 individual LRIs distributed throughout tropical regions of the world's oceans, amassing a total land area of nearly 11,000 km² with approximately 60,740 km of shoreline and housing around 2.6 million people. While intrinsic variables are typically spatially homogenous, LRIs are generally highly spatially clustered throughout the GRID with respect to extrinsic variables. The spatial distribution of LRIs within the GRID was validated using: 1) published data and 2) quantitative accuracy assessments using satellite imagery. Spatial distributions of LRIs captured in the GRID are extremely consistent with those published in the literature (r² = 0.96) and those derived from independent analysis of satellite imagery (r² = 0.94). Finally, the GRID was used to develop an island vulnerability index (IVI) for each LRI on a scale of 0-1 with 0 representing no vulnerability and 1 representing maximum vulnerability. The GRID database is provided as a tab-delimited text file as well as ESRI shapefiles (points and polygons in WGS84 and Mollweide projection) and a comma-separated value file.
This dataset contains experimental data from a one-month aquarium-based bleaching experiment conducted on Large Benthic Foraminifera (Amphistegina lobifera) from 16 November to 16 December 2022 at the Marine Experimental Facility of the Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany. The aim of the experiment was to obtain symbiont-free A. lobifera individuals for future re-inoculation studies and symbiont switching experiments. The foraminifera were originally collected in May 2022 at the Interuniversity Institute for Marine Sciences (IUI) in Eilat, Israel (29°30'07.8N, 34°55'04.9E) and maintained in culture in Germany until the start of the experiment. To assess the effectiveness of two chemical agents—menthol and 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU)—in disrupting symbiosis, photosynthetic efficiency (measured as maximum quantum yield, Fv/Fm) was recorded every other day during the first week of the experiment using a Pulse-Amplitude-Modulated (PAM) fluorometer. Fv/Fm measurements were discontinued after the first week due to complete inhibition of photosynthesis. Symbiont coverage (%) was assessed on day one and then weekly until week four using Confocal Laser Scanning Microscopy (CLSM).
<p>The dataset contains data on arthropods which was derived from DNA metabarcoding. The DNA metabarcoding was performed for samples from coloured canopy Malaise traps, caterpillar traps, branch sampling and blue and white pan traps. The traps were selected to capture predominantly flying insects, especially butterflies and hoverflies. They were placed as a defined set in four different habitat types: 'Forest (Beech and oak)', 'Centre of a short rotation coppice', 'Margin of a short rotation coppice' and 'Maize field'. There existed three replicates of each habitat type. The coloured canopy Malaise traps were equipped with blue, yellow and white cotton cloth panels (50 x 35 cm) and hung in a wooden frame four metres above ground. The caterpillar traps consisted of a dark-green plastic tarpaulin, which was stretched between trees and tapered towards the ground. At its lowest point, insects were collected with a capture bottle, which was attached to the tarpoulin with a fine gauze. The branch sampling was conducted by tabbing 100 tree branches and shaking ten trees at each site. In the corn fields, the shaking was replaced by another interval of tabbing. The blue and white pan traps were placed next to each other on a wooden table, one metre above ground. The pans were enlarged at the top with fine gauze to prevent from overfloating in the case of rain. All insects were captured and stored in 96.6% ethanol. The traps were operated in 15-day sampling intervals, each in June, July and August 2021. For DNA metabarcoding, the samples of each sampling interval and method were compiled. The DNA metabarcoding was performed following the method of Hausmann et al (2020): 'Toward a standardized quantitative and qualitative insect monitoring scheme. Ecology and Evolution, 10, 4009–4020'. Briefly, a lysis volume of 5-10 ml of the dried and homogenized composite samples was used for DNA extraction. The mitochondrial Cytochrome c Oxidase subunit I gene (COI) was amplified for species identification (see Leray et al. (2013): 'A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Frontiers in Zoology, 10, 34' and Morinière et al. (2016): 'Species Identification in Malaise Trap Samples by DNA Barcoding Based on NGS Technologies and a Scoring Matrix. PLOS ONE, 11, e0155497'. The resulting metabarcoding data contains the OTU sequences from the samples and their corresponding identities from the Barcode of Life database (BOLD, Ratnasingham and Hebert 2007), the database of the National Center for Biotechnology Information (NCBI) and the Ribosomal Database Project (RDP) classifier. For the two databases, the overlap between the OTU sequence and the database entry determined the determination depth (97-100%: species, 95-97%: genus; 90-95%. family, 85-90%: order, 80-85%: class, 75-80%: phylum, <75%: domain). Each taxonomic determination level of the RDP-classifier is additionally displayed with a bootstrap value. The table also provides data on the red list Germany and Bavaria for each entry.</p><p>The dataset is used in: Hoffmann, L. & Stoll, S. (2025) Catch effectiveness, complementarity and costs of five sampling techniques for flying insects across different land use types. Insect Conservation and Diversity, 1–12. Available from: https://doi.org/10.1111/icad.12839</p>
Durch die Aufnahme von anthropogenem CO2 ist eine Verringerung des ph Wertes im Ozean zu erwarten Dies könnte weitrechende Auswirkungen auf Korallenriffe haben und ist daher von großem wissenschaftlichen Interesse. Die Mehrzahl der bisher durchgeführten Studien zur Ozeanversauerung wurde anhand vereinfachter Systeme im Labor vorgenommen. Obwohl diese Studien wichtig und aufschlussreich waren, wiesen sie jedoch Einschränkungen auf, im Besonderen hinsichtlich natürlicher Veränderungen, Beobachtungsdauer, Altersverteilung, genetischer Vielfalt und Wechselbeziehungen zwischen einzelnen Korallenarten. Eine Möglichkeit diese Einschränkungen zu umgehen, ist die Untersuchung von Korallenriffen in der Umgebung von submarinen CO2-Austritten. Der erhöhte CO2 im Meerwasser dort sorgt für pH und Temperaturbedingungen wie sie als Folge der Ozeanversauerung bis zum Jahr 2100 vorausgesagt werden. Drei solche Korallenriffe in Papua Neu Guinea, Japan und den Nördlichen Marianen-Inseln wurden untersucht und interessanter Weise fiel die Auswirkung der Ozeanversauerung an jedem der Standorte unterschiedlich aus. Eine mögliche Erklärung für die beobachteten Unterschiede könnte sein, dass die Studien nicht alle Parameter berücksichtigten, die sich nachteilig auf die Gesundheit von Korallen auswirken. Im Allgemeinen werden submarine CO2-Austritte von submarinen Hydrothermalquellen mit Temperaturen bis zu 100 °C begleitet. Der hydrothermale Eintrag induziert steile Temperaturgradienten und erhöht die Konzentrationen von Schwermetallen in dem zu untersuchenden Gebiet. Diese zwei Effekte gilt es bei einer solchen Untersuchung zur Ozeanversauerung in Betracht zu ziehen. In diesem Sinne dient die geplante internationale Zusammenarbeit dem Ziel, ein Expertenteam aus der Aquatischen Chemie und der Korallenphysiologie zusammenzubringen, um detaillierte chemische und biologische Untersuchungen an Korallenriffen mit CO2-Austritten vorzunehmen, um die Auswirkungen der Ozeanversauerung besser zu verstehen.
Weltweit erleben Korallenriffe einen Niedergang. Die langfristigen Folgen dieses Rückgangs der Korallenriffe sind noch ungewiss. Es ist dagegen klar, dass Millionen von Menschen für ihr Überleben auf dieses am weitesten entwickelte Ökosystem in niederen Breiten angewiesen sind. Anthropogen bedingte globale Veränderungen wie die globale Erwärmung, die Versauerung der Ozeane und die Verschlechterung der Wasserqualität (Eutrophierung) wurden als mögliche Schuldige für den Niedergang der Korallenriffe identifiziert. Das Zusammenspiel dieser Faktoren ist jedoch unbekannt und verschiedene Studien deuten darauf hin, dass sie die Entwicklung der Korallenriffe hemmen oder fördern können. Ein Problem ist das Fehlen von Langzeitaufzeichnungen von Meeresoberflächentemperatur (SST) und -produktivität aus Gebieten mit einer Korallen-Vergesellschaftung, die modernen Riffen ähnelt. In diesem Projekt planen wir diese Rekonstruktion für das Queensland Plateau, welches in der Nähe des heutigen Großen Barriere Riffs liegt. Es ist bekannt, dass sich die Korallenriffe in dieser Region bis zum späten Miozän (10-5,5 Ma) ausgedehnt haben. Danach kam es während der mittelpliozänen Warmzeit (3,0-3,5 Ma) zu einer Reduktion der Fläche des Riffsystems. Wir planen drei Biomarker (UK37', TEX86, LDI) zur Rekonstruktion der Meeresoberflächentemperatur zu verwenden. Zur Rekonstruktion der Produktivität werden neue, korallenspezifische Stickstoffisotope mit den Biomarkern und korallen-basierten Ba/Ca verglichen werden. Unsere Pilotdaten zeigen, dass sowohl Meeresoberflächentemperaturen als auch die Produktivität während des Mittel-Pliozäns hoch waren, während nur SSTs während des späten Miozäns hohe Werte zeigen. Diese vorläufigen Daten deuten darauf hin, dass hohe SSTs in Kombination mit einer erhöhten Produktivität während des mittleren Pliozäns die Reduktion des Riffwachstums auf dem Queensland Plateau verursacht haben könnten. Um diese Hypothese zu überprüfen ist es essentiell Daten in höherer Auflösung zu generieren, um die Wechselwirkung von Faktoren zu bestimmen, die zum Verlust von Korallenriffen in der Vergangenheit geführt haben und potentiell in der Zukunft führen werden.
Vom größten Riffkomplex des Atlantischen Ozeans vor der Küste von Belize (Zentralamerika) liegen bislang keine historischen Klimadaten aus Korallen vor. In dem hier beantragten Projekt sollen 18 bereits vorliegende Bohrkerne aus massiven Korallen von Belize sklerochronologisch und geochemisch untersucht werden. Variationen der Wachstumsraten und Schwankungen in der isotopischen Zusammensetzung von Kohlenstoff und Sauerstoff in den Korallenskeletten sollen ermittelt werden, um eine Klimageschichte der letzten 150-200 Jahre für die Region aufzustellen. Da die Kerne in unterschiedlichen Rahmenbedingungen (offenmarine, lagunäre und landnahe Position; unterschiedliche Wassertiefen) genommen wurden, sollte es weiterhin möglich sein, Einflüsse lokaler Variationen von Umweltparametern wie Temperatur, Salinität, Nährstoffgehalten und Licht zu entziffern. Die Ergebnisse dieser Studie sollen mit publizierten historischen Klimadaten des COADS (comprehensive ocean-atmosphere data set) Datensatzes verglichen werden. Weiterhin ist geplant, die Daten mit anderen im Atlantik im Bereich der Sklerochronologie tätigen Arbeitsgruppen auszutauschen, um einen Beitrag zur Rekonstruktion der Veränderlichkeiten von Meeresströmungen und Klima im karibisch-atlantischen Raum zu leisten.
During the Ordovician a massive diversification occurred within reef structures and habitats. In this time interval microbial bioherms were generally replaced by more complex metazoan-dominated structures. These changes were accompanied by a dramatic diversification of the entire reef-fauna. Here I will test the hypothesis, that ecosystem-inherent factors prompted the diversification and the expansion of Ordovician reefs. Hypothetically, new types of organisms massively, and largely independent from climatic and physicochemical changes, influenced the susceptibility, and the structural differentiation of Ordovician reefs. In order to test this hypothesis, a detailed characterization of the evolution of fossil communities within Ordovician reef-systems is necessary. A sedimentological microfacies analysis, and a taxon-quantitative analysis of specific Ordovician reef structures is intended, which will identify crucial diversification drivers. A comparison of changes in the fossil communities of Ordovician reefs with known physicochemical changes will identify the role of organisms itself during the course of the diversification.
| Organisation | Count |
|---|---|
| Bund | 121 |
| Land | 5 |
| Weitere | 1 |
| Wissenschaft | 144 |
| Type | Count |
|---|---|
| Daten und Messstellen | 103 |
| Ereignis | 27 |
| Förderprogramm | 86 |
| Mitmachportal | 1 |
| Taxon | 1 |
| Text | 10 |
| unbekannt | 3 |
| License | Count |
|---|---|
| Geschlossen | 13 |
| Offen | 123 |
| Unbekannt | 94 |
| Language | Count |
|---|---|
| Deutsch | 102 |
| Englisch | 139 |
| Resource type | Count |
|---|---|
| Archiv | 5 |
| Bild | 3 |
| Datei | 32 |
| Dokument | 65 |
| Keine | 52 |
| Webseite | 117 |
| Topic | Count |
|---|---|
| Boden | 131 |
| Lebewesen und Lebensräume | 230 |
| Luft | 149 |
| Mensch und Umwelt | 225 |
| Wasser | 207 |
| Weitere | 230 |