In this study the authors looked at sustainable ways to clean up oil spills that harm marine life. Using water spangle leaves and milk week the authors looked at the ability to recovery oil from both fresh and salt water and the ability to reuse the organic material to clean up spills. Their results show promise to help find a sustainable, eco-friendly way to clean up oil spills and protect marine life and habitats.
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Effects of Ocean Acidification on Marine Calcium Carbonate
Industrialization has transformed human life and improved it for many. Nonetheless, a side effect has been an increase in chemical waste, which when not disposed of properly, has detrimental effects on surrounding habitats. An increase in ocean acidification could potentially affect many forms of life, disrupting the ecological balance in unforeseeable ways. In this article the authors explore the effect of acidification on corals and shells, and observe that an increase in ocean acidity has a significant effect on corals, but not shells. This illustrates how acidification could negatively affect marine life, and calls our attention to managing the factors that contribute to increasing the pH of the Earth's water bodies.
Read More...Using machine learning to develop a global coral bleaching predictor
Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.
Read More...Observing how the distance from the mouth of a Bahamian mangrove affects biodiversity
In this study, the biodiversity of marine animals was studied at different locations along a mangrove, which is a salt-tolerant shrub with elaborate root structures that is found on tropical coastlines.
Read More...Differences in the effect of copper sulfate on the mortality rate of Ostracod and Daphnia
Chemical pollution can have significant effects on freshwater organisms. In this study, the effect of copper sulfate on the survival of Daphnia pulex and Ostracoda was investigated.
Read More...Enhancing marine debris identification with convolutional neural networks
Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.
Read More...Idotea balthica comparison: Anatomy, locomotion, and seaweed preference of Massachusetts isopods
Here the authors examined a population of Massachusetts marine isopods, seeking to classify them based on comparison of their morphology, movement, and seaweed preference compared to those of known species. In this process they found that they were most similar to Idotea balthica. The authors suggest that this knowledge combined with monitoring populations of marine biology such as these isopods in different physical and ecological areas can provide useful insight into the effects of climate change.
Read More...Identifying shark species using an AlexNet CNN model
The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.
Read More...How planarians are affected by mouthwash and cough syrup
Since cough syrup and mouthwash are commonly used items and often end up flushed down the drain or toilet, they can eventually find their way into into freshwater waterways which can be harmful to many marine organisms, such as planarians (aquatic flatworms). To investigate the effects of these substances on planarians, the authors considered different concentrations of Listerine mouthwash and Robitussin syrup along with their active ingredients. By using a behavioral assay, they identified that the active ingredients of cough syrup detrimentally affect planarian behavior. They suggest that these findings could be used to guide disposal methods to lessen detrimental effects on aquatic life.
Read More...A Quantitative Analysis of the Proliferation of Microplastics in Williamston’s Waterways
Plastic debris can disrupt marine ecosystems, spread contaminants, and take years to naturally degrade. In this study, Wu et al aim to establish an understanding of the scope of Williamston, Michigan’s microplastics problem, as well as to attempt to find the source of these plastics. Initially, the authors hypothesize that the Williamston Wastewater Treatment Plant was the primary contributor to Williamston’s microplastics pollution. Although they find a general trend of increasing concentrations of microplastics from upstream to downstream, they do not pinpoint the source of Williamston’s microplastics pollution in the present research.
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