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.