Q10.A classification system that groups organisms together based on the similarity of their observable characteristics is known as-
(A) Phylogenetic system
(B) Phenetic system
(C) Phyletic system
(D) Complement system
Phenetic system (B) groups organisms based on observable characteristic similarity.
This numerical taxonomy emphasizes overall phenotypic resemblance without considering evolutionary descent.
Option Breakdown
Phylogenetic system (A): Classifies based on evolutionary relationships and common ancestry, using cladograms or phylogenetic trees derived from shared derived characters (synapomorphies). Focuses on monophyletic groups reflecting divergence history.
Phenetic system (B): Groups organisms by overall similarity of observable traits (morphology, anatomy, physiology) via numerical analysis (e.g., distance matrices, cluster analysis). Produces phenograms; ignores homology vs. analogy, prioritizing phenotypic resemblance.
Phyletic system (C): Alternative term for phylogenetic or evolutionary classification emphasizing ancestral lineage and gradual divergence. Sometimes used interchangeably with phylogenetic but less common in modern taxonomy.
Complement system (D): Immune cascade of ~30 plasma proteins activated by pathogen recognition, leading to opsonization, inflammation, and membrane attack complex formation. Not a classification system; part of innate immunity.
Introduction to Biological Classification Systems
Phenetic system organisms observable characteristics defines numerical taxonomy grouping taxa by phenotypic similarity scores from multiple measurable traits. Unlike evolutionary approaches, phenetics treats all characters equally regardless of evolutionary weight. Essential for NEET, CSIR-NET life sciences exams and systematic biology studies.
Core Principles of Each System
Phenetic classification quantifies overall resemblance:
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Similarity matrices: Calculate distance metrics (e.g., Euclidean, Manhattan) across 50+ characters.
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Clustering methods: UPGMA, single-linkage produce phenograms showing similarity clusters.
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Character coding: Binary (presence/absence) or multistate; no polarity assessment.
