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Abstract: Understanding the causes and consequences of dispersal is a prerequisite for the effective management of natural populations. Rather than treating dispersal as a fixed trait, it should be considered a plastic process that responds to both genetic and environmental conditions. Here, we consider how the ambient temperature experienced by juvenile Erigone atra, a spider inhabiting crop habitat, influences adult dispersal. This species exhibits 2 distinct forms of dispersal, ballooning (long distance) and rappelling (short distance). Using a half-sib design we raised individuals under 4 different temperature regimes and quantified the spiders' propensity to balloon and to rappel. Additionally, as an indicator of investment in settlement, we determined the size of the webs build by the spiders following dispersal. The optimal temperature regimes for reproduction and overall dispersal investment were 20 °C and 25 °C. Propensity to perform short-distance movements was lowest at 15 °C, whereas for long-distance dispersal it was lowest at 30 °C. Plasticity in dispersal was in the direction predicted on the basis of the risks associated with seasonal changes in habitat availability; long-distance ballooning occurred more frequently under cooler, spring-like conditions and short-distance rappelling under warmer, summer-like conditions. Based on these findings, we conclude that thermal conditions during development provide juvenile spiders with information about the environmental conditions they are likely to encounter as adults and that this information influences the spider's dispersal strategy. Climate change may result in suboptimal adult dispersal behavior, with potentially deleterious population level consequences.
Die Laser Mikrodissektion konnte in der vorliegenden Arbeit als geeignetes Tool für Expressionsanalysen pflanzlicher Gewebe weiterentwickelt werden. Nach einer umfangreichen Optimierung der Technik und Anpassung an die jeweiligen Gegebenheiten der zu analysierenden pflanzlichen Gewebe konnten unterschiedliche physiologische Fragestellungen an verschiedenen Pflanzen bearbeitet werden. Methodische Fortschritte Bei den Arbeiten an infiltrierten Arabidopsis-Pflanzen zeigten sich die methodischen Verbesserungen besonders deutlich: i. Die Zeit der Probengenerierung konnte um 60 80 % reduziert werden, wobei gleichzeitig die Qualität und Quantität der isolierten RNA erheblich verbessert wurden. ii. Dadurch konnte auf die in Deeken et al. (2008) beschriebene Voramplifikation, die stets zum Verlust niedrig exprimierter Gene führt, verzichtet und eine deutlich größere Zahl an im Phloem exprimierten Genen identifiziert werden. iii. Dass dabei 95 % der bei Deeken et al. beschriebenen Phloem-Gene wiedergefunden wurden, zeigt die hohe Reproduzierbarkeit der LMPC-Technik, die durch die Optimierung erreicht werden konnte. Pathogenantwort im Arabidopsis-Phloem iv. Die Laser Mikrodissektion konnte entsprechend i iii eingesetzt werden, um Phloem-Proben von Arabidopsis-Blütenstielen nach Pathogenbefall zu sammeln. v. Bei der Suche nach entsprechenden Phloem-mobilen Signalen, die in systemischen Geweben zur Auslösung der SAR führen, zeigte sich, dass im Phloem der Arabidopsis-Blütenstiele v. a. der Jasmonsäureweg angeschaltet wird. SAR-Marker fanden sich kaum induziert. vi. Im Vergleich der Mikroarray- und qPCR-Ergebnisse wird deutlich, dass mittels LMPC die Vorgänge im Phloem deutlich besser aufgelöst werden können, da die Untersuchungen an kompletten Blütenstielen deutliche Abweichungen gegenüber den Phloem-Arrays aufwiesen. Die Analysen der Mikroarrays sowie die zugehörigen Zeitreihenexperimente sind noch nicht abgeschlossen. Pappel-Holzstrahlen als Schaltstelle der saisonalen Umsteuerung vii. Die Laser Mikrodissektion kann alternativ auch in einem inversen Ansatz angewendet werden. viii. Über auf diese Weise angereicherte Holzstrahlen der Pappel war es möglich, tiefgreifende Einblicke in die Saisonalität der Pappel zu erlangen. ix. Zusammen mit Metabolit- und qPCR-Analysen lieferten diese Ergebnisse einen zeitlichen Ablaufplan der zugrundeliegenden physiologischen Prozesse, insbesondere bei der Umsteuerung von der Dormanz zur Wiederaufnahme des aktiven Wachstums im Frühjahr.
Background: Infections are a leading cause of refugee morbidity. Recent data on the rate of airway infections and factors influencing their spread in refugee reception centers is scarce. Methods: A retrospective, cross-sectional study of de-identified medical records with a focus on respiratory infections in underage refugees was conducted at two large German refugee reception centers. Results: In total, medical data from n = 10,431 refugees over an observational period of n = 819 days was analyzed. Among pediatric patients (n = 4289), 55.3% presented at least once to the on-site medical ward with an acute respiratory infection or signs thereof. In 38.4% of pediatric consultations, acute airway infections or signs thereof were present. Airway infections spiked during colder months and were significantly more prevalent amongst preschool and resettled children. Their frequency displayed a positive correlation with the number of refugees housed at the reception centers. Conclusions: We show that respiratory infections are a leading cause for morbidity in young refugees and that their rate is influenced age, season, status, and residential density. This illustrates the need to protect refugee children from contracting airway infections which may also reduce the spread of coronavirus disease 2019 (COVID-19) during the current pandemic.
West African savannas are severely threatened with intensified land use and increasing degradation. Bees are important for terrestrial biodiversity as they provide native plant species with pollination services. However, little information is available regarding their mutualistic interactions with woody plant species. In the first network study from sub-Saharan West Africa, we investigated the effects of land-use intensity and climatic seasonality on plant–bee communities and their interaction networks. In total, we recorded 5686 interactions between 53 flowering woody plant species and 100 bee species. Bee-species richness and the number of interactions were higher in the low compared to medium and high land-use intensity sites. Bee- and plant-species richness and the number of interactions were higher in the dry compared to the rainy season. Plant–bee visitation networks were not strongly affected by land-use intensity; however, climatic seasonality had a strong effect on network architecture. Null-model corrected connectance and nestedness were higher in the dry compared to the rainy season. In addition, network specialization and null-model corrected modularity were lower in the dry compared to the rainy season. Our results suggest that in our study region, seasonal effects on mutualistic network architecture are more pronounced compared to land-use change effects. Nonetheless, the decrease in bee-species richness and the number of plant–bee interactions with an increase in land-use intensity highlights the importance of savanna conservation for maintaining bee diversity and the concomitant provision of ecosystem services.
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.