Fundamentals of Plant-Parasitic Nematode Diagnostics, Systematics, and Classification
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 December 2025
- ISBN 9780443451362
- Binding Paperback
- No. of pages280 pages
- Size 235x191 mm
- Weight 450 g
- Language English 700
Categories
Long description:
Fundamentals of Plant-Parasitic Nematode Diagnostics, Systematics and Classification offers a comprehensive and authoritative resource for researchers, students, and professionals engaged in nematode biosystematics. The microscopic size, morphological plasticity, and broad ecological diversity have long complicated the efforts to accurately identify nematode species and reconstruct their evolutionary relationships. Unlike many other animal groups, the full extent of nematode species richness remains largely unknown, and aside from rare fossilized specimens preserved in amber, paleontological evidence is minimal.
This book confronts these challenges by integrating recent advances in molecular systematics with traditional taxonomic frameworks to build a unified reference dataset. It critically surveys the historical classification models and establishes a solid foundation for advancing research on nematode evolution and phylogeny. Rather than concentrating on isolated taxa, it presents a broad conceptual toolkit-encompassing diagnostic techniques, hierarchical organizations, and evolutionary analyses-that directly supports the management of character data and robust taxonomic publications. With its interdisciplinary scope and emphasis on both theoretical and practical diagnostics, this book is poised to become an essential reference for those working in agriculture, plant protection, and nematology.
Table of Contents:
1. Introduction
2. General principles for nematode taxonomy and systematics
3. Character and character types
4. Identification of nematodes
5. Note on molecular phylogeny and evolutionary history
6. Classification of plant-parasitic nematodes
7. Representation and management of taxonomic data
