Leveraging Bioinformatics to Understand Biological Diversity for Advancing Human Welfare

J. Swamy

Abstract: Biological diversity underpins ecosystem stability, agricultural productivity, disease regulation and numerous ecosystem services essential to human survival. However, accelerating biodiversity and genetic diversity loss driven by climate change, habitat fragmentation, pollution, and anthropogenic pressures presents a pressing threat to human welfare. Bioinformatics the integration of high-throughput sequencing, data science, and predictive modelling has transformed how researchers quantify, monitor and act on biodiversity information. This paper synthesizes state-of-the-art bioinformatics methodologies (genomic sequencing, DNA metabarcoding, environmental DNA (eDNA) analytics, metagenomics, landscape/population genomics, and biodiversity informatics), illustrates their application using simulated datasets and demonstrates direct implications for human welfare across conservation, agriculture, and environmental health domains. We applied standard bioinformatics pipelines (sequence QC, taxonomic assignment, diversity index computation, population genomic statistics, and species distribution modelling) and combined them into a workflow for integrated biodiversity assessment. Recent advances in multimarker metabarcoding and long-read eDNA improve taxonomic resolution and detection of cryptic/rare taxa, permitting more sensitive biodiversity surveillance in aquatic and terrestrial systems. Metagenomic profiling of soils and waters provides early indicators of ecosystem degradation with consequences for water quality, crop productivity and zoonotic disease risk, linking microbial diversity explicitly to services that influence human well-being. Population and conservation genomics tools identify loss of genetic diversity and local adaptation patterns, information crucial for resilient resource management and restoration planning. Machine learning and AI enhance species identification, accelerate habitat suitability forecasts, and improve threat-prediction models used in conservation decision support systems. Our results show that integrating genomic and ecological datasets yields more robust biodiversity assessments compared to single-axis monitoring; for example, combining genomic resilience metrics with species distribution models improves predicted persistence under climate scenarios. We recommend scaling national genomic monitoring efforts, adopting multimarker eDNA surveillance and embedding AI-assisted bioinformatics pipelines into conservation and public health frameworks to directly advance human welfare.

Keywords: Bioinformatics, Biological Diversity, Metagenomics, DNA Barcoding, Species Distribution Models, Conservation Genomics, Ecosystem Health

How to Cite?: J. Swamy, "Leveraging Bioinformatics to Understand Biological Diversity for Advancing Human Welfare", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 1056-1061, https://www.ijsr.net/getabstract.php?paperid=SR26217131032, DOI: https://dx.dx.doi.org/10.21275/SR26217131032

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