Surgical cotton production has drastically been increased in the past few years due to excessive use by medical health professionals especially in countries like India, which is among the top three exporters of cotton worldwide. The effluent generated from surgical cotton industries differ from textile effluents by the conspicuous absence of dyeing chemicals. This wastewater has a high concentration of suspended particles, COD, dissolved ions, organic carbon, and alkaline pH. Several studies have been published on the treatment of textile effluents and the degradation of dyeing chemicals, while the treatment studies on surgical cotton wastewater have been rarely reported in spite of their potential to cause pollution in receiving land/water bodies. Activated sludge microbes have been extensively studied and well documented in the treatment of several industrial effluent but does not match to the production of valuable biomass from algae. The global energy demand has prompted the scientific community to investigate and explore the possibility of using algae for energy production with simultaneous wastewater treatment. To the best of the authors’ knowledge, no research articles have been published which compare the effectiveness of activated sludge microorganisms, microalgae, and macroalgae in removing contaminants from real wastewater. To date, there is a knowledge gap in understanding and selecting the right choice of biological system for effective and economical effluent treatment. In an attempt to minimize this gap, carbon removal by microalgae, macroalgae, and activated sludge microbes were investigated on real effluent from surgical cotton industries. It was observed that the strain of Chlorella vulgaris could dissipate 83% of COD from real wastewater, while consortia of macroalgae (consisting predominantly of Ulvaceae and Chaetomorpha) and activated sludge microbes could remove 81% and 69% of the carbon, respectively. The microalgal growth (in terms of wet weight) increased from 0.15 to 0.3 g, whereas the macroalgal wet weight increased from 1.5 to 3 g in over 7 days of batch experiments conducted in triplicates. This indicated the superlative performance of microalgae over activated sludge microbes in carbon dissipation.
Genetic mechanisms determining habitat selection and specialization of individuals within species have been hypothesized, but not tested at the appropriate individual level in nature. In this work, we analyzed habitat selection for 139 GPS-collared caribou belonging to 3 declining ecotypes sampled throughout Northwestern Canada. We used Resource Selection Functions comparing resources at used and available locations. We found that the 3 caribou ecotypes differed in their use of habitat suggesting specialization. On expected grounds, we also found differences in habitat selection between summer and winter, but also, originally, among the individuals within an ecotype. We next obtained Single Nucleotide Polymorphisms (SNPs) for the same caribou individuals, we detected those associated to habitat selection, and then identified genes linked to these SNPs. These genes had functions related in other organisms to habitat and dietary specializations, and climatic adaptations. We therefore suggest that individual variation in habitat selection was based on genotypic variation in the SNPs of individual caribou, indicating that genetic forces underlie habitat and diet selection in the species. We also suggest that the associations between habitat and genes that we detected may lead to lack of resilience in the species, thus contributing to caribou endangerment. Our work emphasizes that similar mechanisms may exist for other specialized, endangered species. 相似文献
Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth. 相似文献