Harnessing RStudio package for ecological insights: Monitoring Diatoms with DiaThor

Overview

This study demonstrates the application of the DiaThor package within the RStudio environment for diatom-based biomonitoring and water quality assessment. Diatoms serve as ecological indicators, and their taxonomic composition informs various standardized indices used to evaluate aquatic ecosystem health. The research addresses the need for streamlined computational workflows in ecological monitoring by leveraging open-source tools to automate index calculation and visualization generation. The work is situated within the broader context of integrating computational methods into ecological practice, specifically targeting the operational challenges of processing diatom assemblage data and interpreting multiple biotic indices simultaneously across sampling sites.

Methods and approach

The study employs RStudio as the computational platform and the DiaThor package as the analytical engine for processing water quality datasets derived from diatom assemblages. A case study approach was adopted, applying the package to empirical datasets to calculate multiple diatom-based ecological indices, including the Indice de Polluosensibilité Spécifique (IPS), Trophic Diatom Index (TDI), Schiefele and Lauterborn Index (SLA), and additional indices not specified in detail. The manuscript provides methodological guidance on data formatting requirements, function implementation within the DiaThor framework, and the generation of graphical outputs. Visualization techniques include bar plots, heatmaps, and radar diagrams, each designed to represent spatial and ecological variation in index values across multiple sample sites.

Key Findings

The application successfully generated a suite of visualizations representing diatom-based indices across sample sites. Bar plots, heatmaps, and radar diagrams were produced, each offering distinct perspectives on index variation and spatial patterns. The visualization outputs facilitated the detection of ecological gradients and enabled efficient interpretation of water quality conditions across the study area. The computational workflow demonstrated reproducibility and automation potential within the R environment. The integration of DiaThor with R's visualization ecosystem proved effective for enhancing data clarity and supporting comparative assessments of water quality based on diatom assemblage composition.

Implications

The study establishes that DiaThor integrated within RStudio provides an efficient and reproducible methodology for ecological data processing in biomonitoring contexts. The automation capabilities and visualization integration address operational needs in environmental monitoring workflows, potentially reducing manual calculation burdens and standardizing analytical approaches. The demonstration of open-source tools for diatom-based assessment suggests broader applicability for routine monitoring programs and comparative studies. The work contributes to the ongoing transition toward computational methods in ecological assessment, highlighting the capacity of open-source software ecosystems to support standardized biomonitoring practices and facilitate data-driven interpretation of aquatic ecosystem condition.