Show HN: Interactive Tree of Life
Recorded: Sept. 8, 2026, 7:08 a.m.
| Original | Summarized |
Interactive Phylo Tree of Life - Ptree Interactive Phylo Tree of Life Taxonomy GBIFKingdom-to-species classification for biodiversity observations Descent OTTEvolutionary branching synthesized from many phylogenetic trees Microbes GTDBBacteria and archaea grouped by genome-based ancestry Specialty WFOPublished plant names resolved under botanical rules Columns Tree Branches “” Gram stain[1] Risk group[1] Temperature[1] pH[1] Breeding frequency[1] Maturity[1] Longevity[1] Development time[1] Offspring count[1] Offspring mass[1] Parental care[1] Ontogeny[1] Sex & mating[1] Reproduction[1] Shape[1] Construction[1] Movement[1] Migration[1] Nutrition[1] Respiration[1] Organization[1] Earliest fossil[1] Setting[1] Position[1] Vertical range[1] Climate[1] Day cycle[1] Light sensing[1] Spread[1] Use & trade[1] Conservation[1] Population trend[1] Linear extent[1] Mass[1] Volume[1] Niche breadth[1] Drought resistance[1] Leaf economymm2mgmgg[1] Gas exchangeµmolmmolmmolm2·s[1] Growth form[1] Persistence[1] Gram-negativeGram-variableGram-positive iNat geomodelMDD ranges Wikipedia FeedingEaten byEatsPreyed upon byPreys onKilled byKillsFood stolen bySteals food fromNourished byNourishesTrophically interacts withFarmed byFarms Drop taxa here to compare Ptree is created by Michael Dayah of Ptable and exists thanks to openly-licensed biodiversity data. |
The provided material details frameworks for biodiversity classification, evolutionary descent tracking, microbial grouping, and the quantitative measurement of various biological and environmental parameters, all structured within a conceptual phylogenetic representation. Taxonomy is established through various global reference systems such as GBIF for kingdom-to-species classification, COL for accepted names and synonyms, and ITIS for government record linking by scientific name, while descent is managed by OTTE for evolutionary branching synthesis and EOL for the hierarchy combining descriptions and traits. Furthermore, resources like NCBI group organisms based on DNA and protein records, and SILVA groups bacteria, archaea, and eukaryotes based on ribosomal RNA. The data structure incorporates a comprehensive set of columns that quantify diverse biological characteristics. These metrics range from developmental and physiological measures such as longevity, development time, offspring counts, mass, volume, and growth forms, to specific environmental factors like temperature, pH, flow rates, vertical range, climate variables, light sensing, and nutritional modes (e.g., photosynthetic versus chemosynthetic). Specific physiological measurements include gas exchange rates, nutrient assimilation efficiency, and material properties such as leaf economics and mass. The document further elaborates on specific biological processes through detailed interaction matrices. These interactions define complex ecological relationships, including feeding dynamics where organisms are categorized by what they eat, what preys upon, and what they are preyed upon by. Relationships are also defined across pathogenic structures, detailing how organisms infect others, the transmission of diseases, and symbiotic arrangements such as mutualism, commensalism, and parasitism, specifying whether entities live inside or on hosts. Specific ecological associations are mapped, describing how species interact with their habitat, including proximity, coexistence, visitation, pollination, dispersal mechanisms, and the creation of habitats. These interactions further categorize relationships like host-parasite dynamics, where information is tracked regarding the reservoir hosts, endoparasites, ectoparasites, and hyperparasites. The structure also defines specific traits across different domains, including morphology (shape, construction), movement (migration, climbing, flying), nutritional modes (herbivorous, omnivorous, predatory), environmental response (drought resistance), and spatial organisation (vertical range, linear extent). The data integrates complex metrics such as mass, area/mass ratios, and various rate calculations for flow and exchange. These variables are applied across different taxa, with categories distinguishing between Gram-negative and Gram-positive bacteria and correlating these classifications with risk groups and specific environmental conditions like temperature ranges. Finally, the text acknowledges the reliance on this structured system by citing Michael Dayah for the creation of Ptree, which utilizes openly licensed biodiversity data to represent the common ancestor and subsequent evolutionary branching. |