Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining.

Microplastic (MP) research faces challenges due to costly, time-consuming, and error-prone analysis techniques. Additionally, the variability in data quality across studies limits their comparability. This study addresses the critical need for reliable and cost-effective MP analysis methods through validation of a semi-automated workflow, where environmentally relevant MP were spiked into and recovered from marine fish gastrointestinal tracts (GITs) and blue mussel tissue, using Nile red staining and machine learning automated analysis of different polymers. Parameters validated include trueness, precision, uncertainty, limit of quantification, specificity, sensitivity, selectivity, and method robustness. For fish GITs a 95 ± 9 % recovery rate was achieved, and 87 ± 11 % for mussels. Polymer identification accuracies were 76 ± 8 % for fish GITs and 80 ± 13 % for mussels. Polyethylene terephthalate fragments showed more variability with lower accuracies. The proposed validation parameters offer a step towards quality management guidelines, as such aiding future researchers and fostering cross-study comparability.

Comparative analysis of microplastics detection methods applied to marine sediments: A case study in the Bay of Marseille.

An intercomparison exercise on "microplastics in sediment" was carried out by five laboratories using samples collected in the Bay of Marseille in September 2021. The results from different extraction and identification methods varied depending on the type and size classes of MPs, and was better than 80 % for the size class >300 µm and for the fragments. The variability in recovery rates can be attributed to the choice of reagents and extraction protocols. Recovery rates per laboratory were between 47 % and 113 % and the use of ZnCl2 and NaI increased recovery rates by an average of 70 %. The lowest recovery rates (47 and 53 %) were attributed to the reference methods (FTIR and LDIR), conversely the highest (80 and 87 %) were attributed to identification by Nile Red. The average ranged between 23 and 53 items /50 g d.w. with decreases offshore and at greater depth.

Microplastic detection and identification by Nile red staining: Towards a semi-automated, cost- and time-effective technique.

Microplastic pollution is an issue of concern due to the accumulation rates in the marine environment combined with the limited knowledge about their abundance, distribution and associated environmental impacts. However, surveying and monitoring microplastics in the environment can be time consuming and costly. The development of cost- and time-effective methods is imperative to overcome some of the current critical bottlenecks in microplastic detection and identification, and to advance microplastics research. Here, an innovative approach for microplastic analysis is presented that combines the advantages of high-throughput screening with those of automation. The proposed approach used Red Green Blue (RGB) data extracted from photos of Nile red-fluorescently stained microplastics (50-1200 µm) to train and validate a 'Plastic Detection Model' (PDM) and a 'Polymer Identification Model' (PIM). These two supervised machine learning models predicted with high accuracy the plastic or natural origin of particles (95.8%), and the polymer types of the microplastics (88.1%). The applicability of the PDM and the PIM was demonstrated by successfully using the models to detect (92.7%) and identify (80%) plastic particles in spiked environmental samples that underwent laboratorial processing. The classification models represent a semi-automated, high-throughput and reproducible method to characterize microplastics in a straightforward, cost- and time-effective yet reliable way.

The cost of a meal: factors influencing prey profitability in Australian fur seals.

Knowledge of the factors shaping the foraging behaviour of species is central to understanding their ecosystem role and predicting their response to environmental variability. To maximise survival and reproduction, foraging strategies must balance the costs and benefits related to energy needed to pursue, manipulate, and consume prey with the nutritional reward obtained. While such information is vital for understanding how changes in prey assemblages may affect predators, determining these components is inherently difficult in cryptic predators. The present study used animal-borne video data loggers to investigate the costs and benefits related to different prey types for female Australian fur seals (Arctocephalus pusillus doriferus), a primarily benthic foraging species in the low productivity Bass Strait, south-eastern Australia. A total of 1,263 prey captures, resulting from 2,027 prey detections, were observed in 84.5 h of video recordings from 23 individuals. Substantial differences in prey pursuit and handling times, gross energy gain and total energy expenditure were observed between prey types. Importantly, the profitability of prey was not significantly different between prey types, with the exception of elasmobranchs. This study highlights the benefit of animal-borne video data loggers for understanding the factors that influence foraging decisions in predators. Further studies incorporating search times for different prey types would further elucidate how profitability differs with prey type.