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.

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.

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.

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.