Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.

Introduction: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths.

Transcriptomic sequencing data illuminate insecticide-induced physiological stress mechanisms in aquatic non-target invertebrates.

Pesticides are major agricultural stressors for freshwater species. Exposure to pesticides can disrupt the biotic integrity of freshwater ecosystems and impair associated ecosystem functions. Unfortunately, physiological mechanisms through which pesticides affect aquatic organisms are largely unknown.

Impacts of multiple anthropogenic stressors on the transcriptional response of Gammarus fossarum in a mesocosm field experiment.

Background: Freshwaters are exposed to multiple anthropogenic stressors, leading to habitat degradation and biodiversity decline. In particular, agricultural stressors are known to result in decreased abundances and community shifts towards more tolerant taxa. However, the combined effects of stressors are difficult to predict as they can interact in complex ways, leading to enhanced (synergistic) or decreased (antagonistic) response patterns.