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Background: Understanding biodiversity patterns is a central topic in biogeography and ecology, and it is essential for conservation planning and policy development. Diversity estimates that consider the evolutionary relationships among species, such as phylogenetic diversity and phylogenetic endemicity indices, provide valuable insights into the functional diversity and evolutionary uniqueness of biological communities. These estimates are crucial for informed decision-making and effective global biodiversity management. However, the current methodologies used to generate these metrics encounter challenges in terms of efficiency, accuracy, and data integration.
Results: We introduce PhyloNext, a flexible and data-intensive computational pipeline designed for phylogenetic diversity and endemicity analysis. The pipeline integrates GBIF occurrence data and OpenTree phylogenies with the Biodiverse software. PhyloNext is free, open-source, and provided as Docker and Singularity containers for effortless setup. To enhance user accessibility, a user-friendly, web-based graphical user interface has been developed, facilitating easy and efficient navigation for exploring and executing the pipeline. PhyloNext streamlines the process of conducting phylogenetic diversity analyses, improving efficiency, accuracy, and reproducibility. The automated workflow allows for periodic reanalysis using updated input data, ensuring that conservation strategies remain relevant and informed by the latest available data.
Conclusions: PhyloNext provides researchers, conservationists, and policymakers with a powerful tool to facilitate a broader understanding of biodiversity patterns, supporting more effective conservation planning and policy development. This new pipeline simplifies the creation of reproducible and easily updatable phylogenetic diversity analyses. Additionally, it promotes increased interoperability and integration with other biodiversity databases and analytical tools.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11165860 | PMC |
http://dx.doi.org/10.1186/s12862-024-02256-9 | DOI Listing |
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