Digesting the Indigestible: Microplastic Extraction from Prawn Digestive Tracts

Microplastics (MPs) have become ubiquitous in the marine environment, and are likely ingested by a broad cross-section of marine life. The extent to which marine organisms ingest MPs is uncertain due to limitations in analytical methods. Effective identification and analysis of ingested MPs is a precursor to understand their impact on marine organisms and their human consumers. This is particularly challenging for crustaceans, due to the chitin present in their exoskeleton and digestive systems, which is resistant to chemical degradation. This study presents a novel application that can efficiently break down the stable organic tissue of banana prawns (Penaeus merguiensis), and subsequently isolate putative MP polymers from the digestive tract without damaging their integrity. Five treatments were examined for their capacity to break down chitin from the prawn digestive system; namely acid, alkaline, oxidant, enzyme and microwave assisted oxidant digestion. Gravimetric and image analysis revealed that the organic tissue of the prawn gastrointestinal tract can be effectively removed by acid, oxidant, and microwave assisted oxidant digestion methods. However, testing on seven reference polymers (polyamide (PA), polyethylene (PE), polyester (PES), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and rayon) revealed significant degradation when exposed to acid digestion. Overall, microwave assisted oxidant digestion achieved the best recovery rate of spiked MPs ( > 90%) with minimal size, shape, and Fourier transform infrared (FTIR) spectral changes for all polymers except for rayon. These results highlight a new direction for tissue removal and MP extraction in crustacean ingestion studies

Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows

Plastics for dinner: Store-bought seafood, but not wild-caught from the Great Barrier Reef, as a source of microplastics to human consumers

Seafood accounts for more than 17% of the global consumption of animal protein, with an excess of 335000 t consumed in Australia throughout 2019-2020. Recently, the presence of microplastics (MPs) within commercial seafood and the potential vectorisation of MPs to human consumers has become a significant concern for the public and the scientific community. Here, four commonly harvested wild-caught marine organisms were assessed for MP presence. These species comprise a significant proportion of the Queensland seafood industry, as well as being highly desirable to Australian consumers. The edible muscle tissue and discarded digestive tissue (GIT) of barramundi (Lates calcifer), coral trout (Plectropomus leopardus), blue leg king prawns (Melicertus latisulcatus), and Ballot's saucer scallops (Ylistrum balloti), were analysed discretely to determine the extent to which these species may be contaminated in the wild (GIT tissue), and the extent to which they themselves may act as a vector for human exposure (edible muscle tissue). Wild-caught seafood was predominantly free of MPs, with digestive tissues from two of ten coral trout containing only two fibres each. All wild-caught muscle tissue samples were free of MPs, as was the GIT of scallops, prawns, and barramundi. On the other hand, fresh, skinless barramundi muscle tissues, purchased from various commercial suppliers, were examined and found to be significantly contaminated with MPs (0.02 - 0.19 MP g-1). Overall, these results highlight the growing consensus that food can become contaminated simply by being prepared in the human environment, and the focus must shift to determining the extent of MP proliferation within the processing and point-of-sale environment

SeeCucumbers: using deep learning and drone iagery to detect sea cucumbers on coral reef flats

Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species