The exogenous application of chemical repellents is widespread in birds, but endogenous production is exceedingly rare. We herein report a new class of avian defensive compounds isolated from the feathers and volatile odor of the crested auklet (Aethia cristatella). Mass spectra indicate that n-hexanal, n-octanal, n-decanal, Z-4-decenal and a 12-carbon unsaturated aldehyde comprise the auklet odorant. Octanal and hexanal are also secreted in the repugnant metasternal gland emissions of heteropteran insects and are known to be potent invertebrate repellents. We suggest that the auklet odorant functions as an ectoparasite repellent and a signal of mate quality. This would represent a rare and direct link between vigor, quality and parasite resistance, one of several putative bases for mate selection. This is the first report of defensive compounds produced by a seabird or colonial bird and one of the few examples of chemical defense in a polar or subpolar marine vertebrate. 相似文献
Toxicity, uptake, and transformation of atrazine [2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine] by three species of poplar tree were assessed. Poplar cuttings were grown in sealed flasks with hydrophonic solutions and exposed to various concentrations of atrazine for a period of two weeks. Toxicity effects were evaluated by monitoring transpiration and measuring poplar cutting mass. Exposure to higher atrazine concentrations resulted in decrease of biomass and transpiration accompanied by leaf chlorosis and abscission. However, poplar cuttings exposed to lower concentrations of atrazine grew well and transpired at a constant rate during experiment periods. Poplar cuttings could take up, hydrolyze, and dealkylate atrazine to less toxic metabolites. Metabolism of atrazine occurred in roots, stems, and leaves and became more complete with increased residence time in tissue. These results suggest that phytoremediation is a viable approach to removing atrazine from contaminated water and should be considered for other contaminants. 相似文献
The sustainable development agenda 2030 calls for achievement of certain targets to ensure access to water and sanitation for all. Multi-stakeholder partnerships and the use of data and modelling tools are conditioning elements for their achievement. In this article, we demonstrate that participatory modelling supports informed and participatory decision making in complex river basins. An adapted companion modelling approach is presented to support collective action by reducing disputes and enhancing collaboration among stakeholders. The co-development and use of empirical models for understanding the complexity of the physical system is combined with the use of role-playing games to ensure the active involvement of stakeholders. The approach is implemented in a top-down water quality planning process in Turkey. Results show its suitability for managing water quality in complex river basins in an inclusive manner and its substantial benefits in developing stakeholders’ capacities and creating a cooperative environment. 相似文献
Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R2?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R2?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R2?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.
Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste... 相似文献