The Smart Soil Organism Detector: An instrument and machine learning pipeline for soil species identification

Towards holistic insect monitoring: species discovery, description, identification and traits for all insects

Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Opinions and Suggestions on Nematode Faunal Analysis

We briefly review the history and development of recognizing nematode assemblages as indicators of environmental conditions. We highlight the effects of spatio-temporal successional changes in nematode assemblages on the auto-regeneration of ecosystem functions after disturbance. We expand on the need for herbivory components in the analysis of soil nematode assemblages in recognition of the important impact of plant parasitism on the resources and productivity of the soil system. Finally, we point out some important areas of research that would enhance the process and value of nematode faunal analysis. We include an evaluation of the current potential for molecular assessment of nematode abundance and function and for the application of artificial intelligence in automated nematode identification.

In situ chlorpyrifos (CPF) degradation by Acrobeloides maximus: Insights from chromatographic analysis

The objective of this study was to evaluate the efficiency of nematodes in zooremediation of chlorpyrifos (CPF), an organophosphate pesticide. The nematode population Acrobeloides maximus (A. maximus) was employed for bioremediation, converting CPF into non-toxic residues. Optimal growth conditions for mass production of A. maximus were achieved by maintaining a temperature of 25 °C, pH 8, and supplementing the culture medium with plant nutrients. The nematodes were then immobilized within sodium alginate beads. The efficacy of the degradation process was assessed using various analytical techniques, including UV-Visible spectroscopy, HPTLC, FTIR, and LC-MS, confirming the successful breakdown of CPF. The bioreactor demonstrated a complete degradation efficiency of CPF exceeding 99%. Additionally, LC-MS analysis was conducted to elucidate the degradation pathway based on the formation of intermediates. These results underscore the potential of A. maximus as a sustainable organism for addressing environmental contamination arising from CPF pesticide.