Smartphone-Based Microalgae Monitoring Platform Using Machine Learning

There is a growing demand for microalgae monitoring techniques since microalgae are one of the most influential underwater organisms in aquatic environments. Specifically, such a technique should be hand-held, rapid, and easily accessible in the field since current methods (benchtop microscopy, flow cytometry, or satellite imaging) require high equipment costs and well-trained personnel. This study’s main objective was to develop a field-deployable microalgae monitoring platform using only a single smartphone and inexpensive acrylic color films. It aimed to evaluate the morphological states of microalgae including stress, cell concentration, and dominant species. Using a smartphone’s white LED flash and camera, the platform detected fluorescence and reflectance intensities from microalgal samples in various excitation and emission color combinations. Multidimensional intensity data were evaluated from the smartphone images and used to train a support vector machine (SVM) based machine learning model to classify various morphological states. The SVM classification accuracies were 0.84-0.96 in classifying four- to five-tier stress types, cell concentration, and dominant species and 0.99-1.00 in classifying two-tier stress types and cell concentrations. Additional field samples were collected from the local pond and independently tested using the laboratory-collected training set, showing two-tier classification accuracies of 0.90-1.00. This platform enables accessible and on-site microalgae monitoring for nonexperts and can be potentially applied to monitoring harmful algal blooms (HABs).University of Arizona12 month embargo; first published 17 August 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Sea urchin repelling Tannin- Fe-III complex coating for ocean macroalgal afforestation

Intense seaweed grazing by sea urchins has destroyed kelp forests and accelerated the transformation of these forests into barren areas known as urchin barrens. Once the sea urchins occupy the barren ground, it becomes more challenging to restore the kelp forests. Although phlorotannin, a primary herbivore defense chemical secreted by kelp, has been reported to discourage feeding activities of marine herbivores but the direct application of naturally extracted phlorotannin does not effectively repel sea urchins. In this study, we applied a simple and green Tannin-Fe-III (TA-Fe-III) coating on substrates as a sea urchin repellent using a cheap, ecofriendly tannin (TA) obtained from biomass as an alternative to phlorotannin. In a model aquarium experiment, most of the sea urchins (Anthocidaris crassispina) in the tank evaded the TA-Fe-III-coated substrates. In field tests with 300 sea urchins, the majority of sea urchins could not crawl over the TA-Fe-III-coated rope for more than 2 h in contrast to the control group. Hence, the safety, cost-effectiveness, and scalability of the TA-Fe-III coating make it a practical candidate to protect the kelp ecosystem from sea urchins. (C) 2020 Elsevier Ltd. All rights reserved.11Nsciescopu

Smartphone-Based Microalgae Monitoring Platform Using Machine Learning

There is a growing demand for microalgae monitoring techniques since microalgae are one of the most influential underwater organisms in aquatic environments. Specifically, such a technique should be hand-held, rapid, and easily accessible in the field since current methods (benchtop microscopy, flow cytometry, or satellite imaging) require high equipment costs and well-trained personnel. This study’s main objective was to develop a field-deployable microalgae monitoring platform using only a single smartphone and inexpensive acrylic color films. It aimed to evaluate the morphological states of microalgae including stress, cell concentration, and dominant species. Using a smartphone’s white LED flash and camera, the platform detected fluorescence and reflectance intensities from microalgal samples in various excitation and emission color combinations. Multidimensional intensity data were evaluated from the smartphone images and used to train a support vector machine (SVM) based machine learning model to classify various morphological states. The SVM classification accuracies were 0.84–0.96 in classifying four- to five-tier stress types, cell concentration, and dominant species and 0.99–1.00 in classifying two-tier stress types and cell concentrations. Additional field samples were collected from the local pond and independently tested using the laboratory-collected training set, showing two-tier classification accuracies of 0.90–1.00. This platform enables accessible and on-site microalgae monitoring for nonexperts and can be potentially applied to monitoring harmful algal blooms (HABs)

Mechanical Stimuli Enhance the Growth of Ulva fasciata (Chlorophyta) Spores

The macroalgal forest is being transformed to barren ground in coastal areas due to climate change. Current recovery technologies are ineffective, and an understanding of the fouling mechanism of macroalgae would assist restoration of macroalgal forests. Here, it is attempted to promote the growth of Ulva fasciata spores using an artificial biofilm composed of alginate and silica particles (the most abundant polymers and inorganics in marine biofilms) by mimicking marine environments. The presence of silica particles in the artificial biofilm increased the average germling length of Ulva spores, suggesting that mechanical stimuli generated by the silica particles enhanced growth of Ulva spores. As the stiffness of the model substrates (polystyrene, hydrophilic silica, hydrophobic silica, PDMS) increased, the germling length of the spores increased irrespective of surface chemistry and hydrophobicity. These results suggest that manipulation of the mechanical stimuli generated by the substrate could enable control of macroalgal fouling.11Nsciescopu

A new approach to the restoration of seaweed beds using Sargassum fulvellum

Seaweed beds are productive marine ecosystems; they provide habitat and serve as spawning, breeding, and feeding sites for fish and shellfish. Seaweed beds are declining with environmental change and pollution. In affected areas, including “urchin barrens” and those affected by “whitening events,” coralline algae appear, preventing the attachment of seaweed spores to the substrate. Many methods have been used to restore seaweed beds, such as those employing artificial reefs, seaweed ropes, spore bags, and transplanted cultures. However, such efforts are insufficient to overcome the disappearance of seaweed beds from coastal areas. This study examined the use of a new technique that involves encapsulating seaweed zygotes with polysaccharide-like alginates to improve their attachment using the brown alga Sargassum fulvellum, which plays an important role in seaweed forests. We tested the efficacy of encapsulated zygotes using polyvinyl chloride (PVC) panels and concrete bricks in the sea. In the laboratory, the germination percentage of encapsulated Sargassum zygotes was 70% ± 1.6%, similar to the rate of unencapsulated zygotes. In the field experiment, PVC panels and concrete bricks were coated with encapsulated and unencapsulated zygotes; the germination density and growth rates of encapsulated zygotes were 4 (p < 0.001) and 7 times (p < 0.016) greater, respectively, than those of unencapsulated zygotes. The germination density and growth rate of encapsulated zygotes on concrete bricks were also greater. Therefore, encapsulation should increase the attachment of seaweed spores in marine environments.11Nsciescopu